CN107810323A - The method and system arranged for generating wind turbine control - Google Patents

Abstract

There is provided a method of generating a control schedule for a wind turbine indicating how the maximum power level of the turbine varies over time, the method comprising: based on measured wind turbine site and/or operational data, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components; applying an optimization function by varying energy capture versus fatigue life consumed by the turbine or the one or more turbine components The trade-off between until the optimized control arrangement is determined, to change the initial control arrangement to determine the optimized control arrangement, the optimization includes: based on the current remaining fatigue life and the changed control arrangement, estimating or the future fatigue life consumed by turbine components within the duration of the changed control arrangement; and constraining the optimization of the control arrangement according to one or more input constraints; wherein the optimization also includes changing the wind turbine an initial value of lifetime, and varying an initial value of a number of component replacements to be performed on one or more components during the schedule to determine a relationship between the number of component replacements of one or more turbine components and a target minimum wind turbine lifetime The combination.

Description

Method and system for generating wind turbine control arrangements

technical field

Embodiments of the invention relate to methods and systems for determining a control schedule for wind turbine power output.

Background technique

FIG. 1A shows a large conventional wind turbine 1 known in the prior art, comprising a tower 10 and a wind turbine nacelle 20 on top of the tower 10 . The wind turbine rotor 30 includes three wind turbine blades 32 each having a length L . Wind turbine rotor 30 may include other numbers of blades 32 , such as one, two, four, five, or more. The blades 32 are mounted on a hub 34 at a height H above the bottom of the tower. Hub 34 is connected to nacelle 20 by a low speed shaft (not shown) extending from the front of nacelle 20 . The low speed shaft drives a gearbox (not shown) which speeds up the rotation and in turn drives a generator within the nacelle 20 to convert the energy extracted from the wind by the rotating blades 32 into an electrical power output. The wind turbine blade 32 defines a swept area A, which is a circular area delimited by the rotating blade 32 . The swept area indicates how much of a given air mass is intercepted by the wind turbine 1 and thus affects the power output of the wind turbine 1 as well as the forces and bending moments experienced by components of the turbine 1 during operation. As shown, the turbines may be located onshore or offshore. In the latter case, the tower will be attached to a monopile tripod grid or other foundation structure, and this foundation may be fixed or floating.

For example, each wind turbine has a wind turbine controller, which may be located at the base of the tower or at the top of the tower. Wind turbine controllers process inputs from sensors and other control systems and generate inputs for actuators such as pitch actuators, generator torque controllers, generator contactors, switches to activate shaft brakes, bias The output signal of the aircraft motor, etc.

FIG. 1B schematically shows an example of a conventional wind park 100 comprising a plurality of wind turbines 110 , the controllers of each of which are in communication with a plant controller PPC 130 . PPC 130 may communicate bi-directionally with each turbine. The turbine outputs power to grid connection point 140 as indicated by bold line 150 . In operation, and assuming wind conditions permit, each of wind turbines 110 will output a maximum active power up to its rated power as specified by the manufacturer.

Figure 2 shows a conventional power curve 55 for a wind turbine plotting wind speed on the x-axis and power output on the y-axis. Curve 55 is a normal power curve for a wind turbine and defines the power output by the wind turbine generator as a function of wind speed. As is known in the art, a wind turbine starts generating power at a cut-in wind speed _Vmin . The turbine is then operated at part load (also called partial load) until the rated wind speed is reached at point VR _. At rated wind speed, rated (or nominal) generator power is achieved and the turbine is operating at full load. For example, the cut-in wind speed in a typical wind turbine may be 3m/s, and the rated wind speed may be 12m/s. Point V _max is the cut-out wind speed, which is the highest wind speed at which the wind turbine can operate while delivering power. At wind speeds equal to or higher than the cut-out wind speed, the wind turbine is switched off for safety reasons, in particular to reduce the loads acting on the wind turbine. Alternatively, the power output can be ramped down to zero power as a function of wind speed.

The rated power of a wind turbine is defined in IEC 61400 as the maximum continuous electrical power output that the wind turbine is designed to achieve under normal operating and external conditions. Large commercial wind turbines are typically designed for a life of 20 to 25 years and are designed to operate at rated power such that the design loads and fatigue life of the components are not exceeded.

The rate of fatigue damage accumulation of individual components in a wind turbine varies greatly under different operating conditions. As the power generated increases, the rate of wear, or damage accumulation, tends to increase. Wind conditions also affect the rate of damage accumulation. For some mechanical components, operation in very high turbulence results in fatigue damage accumulation rates many times higher than operation in normal turbulence. For some electrical components, operation at very high temperatures, possibly caused by high ambient temperatures, results in fatigue damage accumulation rates (such as insulation breakdown rates) many times higher than operation at normal temperatures. As an example, a rule of thumb for generator windings is that a 10°C drop in winding temperature will increase life by 100%.

The annual energy production (AEP) of a wind park is related to the production rate of the wind turbines forming the wind park and typically depends on the annual wind speed at the location of the wind park. For a given wind park, the larger the AEP, the greater the profit for the operator of the wind park and the greater the amount of electrical energy supplied to the grid.

Thus, wind turbine manufacturers and wind park operators are always trying to increase the AEP of a given wind park.

One such approach could be to over-rate the wind turbine under certain conditions, in other words, to allow the wind turbine to operate for a period of time at a power level up to the wind turbine's rated or nameplate power level (shaded area in Figure 2 58) in order to generate more electricity when the wind is high and thus increase the AEP of the wind power plant. In particular, the term "over-rating" should be understood to mean the generation of power in excess of the rated active power during full-load operation by controlling turbine parameters such as rotor speed, torque or generator current. An increase in speed demand, torque demand, and/or generator current demand will increase the additional power generated through overrating, while a decrease in speed demand, torque demand, and/or generator current demand will decrease the amount of power generated through overrating additional power. It should be understood that overrating applies to real power, not reactive power. When overrating a turbine, the turbine is run more aggressively than under normal conditions and the generator has a higher power output than rated for a given wind speed. For example, overrated power levels can be as high as 30% above rated power output. This allows greater power extraction when it is beneficial to the operator, especially when external conditions such as wind speed, turbulence and electricity prices allow for more profitable generation.

Overrating causes higher wear or fatigue on the components of the wind turbine, which may lead to premature failure of one or more components and the need to shut down the turbine for maintenance. Thus, overrating is characterized by transient behavior. When a turbine is overrated, it may last for as little as a few seconds, or it may last for an extended period of time if wind conditions and component fatigue life favor overrating.

While overrating allows turbine operators to increase AEP and otherwise modify power generation to meet their requirements, there are several problems and drawbacks associated with overrating wind turbines. Wind turbines are typically designed to operate at a given nominal rated or nameplate power level and are certified to operate for a number of years, eg, 20 or 25 years. Therefore, if the wind turbine is overrated, the lifetime of the wind turbine may be shortened.

The present invention seeks to provide a flexibility for turbine operators to have their turbines operate in a manner consistent with their requirements, for example by returning an optimized AEP.

Contents of the invention

The invention is defined in the independent claims, to which reference will now be made. Preferred features are set out in the dependent claims

Embodiments of the present invention seek to increase the flexibility available to turbine operators when employing control methods that compromise energy capture and fatigue loads. An example of this control method is the use of overrating.

According to a first aspect of the present invention there is provided a method of generating a control schedule for a wind turbine indicating how the maximum power level of the turbine varies over time, the method comprising:

receiving an input indicative of a target minimum wind turbine lifetime;

determining a value indicative of a current remaining fatigue life of the wind turbine or one or more turbine components based on the measured wind turbine site and/or operational data;

The parameters of the initial predefined control arrangement specifying how the maximum power level of the turbine varies over time are varied by:

i) adjust the parameters of the initial predefined control arrangement;

ii) estimating, based on the changed control arrangement, the future fatigue life consumed by the turbine or one or more turbine components for the duration of the changed control arrangement;

as well as

iii) Repeating steps (i) and (ii) until the estimated future fatigue life consumed by each of the one or more turbine components or the wind turbine is sufficient to allow the target minimum wind turbine life to be reached.

The parameters may be varied until the estimated future fatigue life consumed by the heaviest loaded components is sufficient to allow just reaching the target minimum wind turbine life, or in other words until the total fatigue life consumed is substantially the same as the target minimum wind turbine life until. This may be achieved based on a predetermined margin of the target minimum wind turbine lifetime (eg, within 0 to 1 month, within 0 to 3 months, within 0 to 6 months, or within 0 to 12 months of the target).

Optionally, step (iii) also entails maximizing energy capture over the lifetime of the turbine.

Optionally, the control arrangement is indicative of an amount by which the wind turbine may be overrated to power above its rated power.

Optionally, the method further comprises receiving, for each of one or more of the turbomachine components, an input indicative of a maximum number of allowable replacements for that turbomachine component. Then, step (i) may also include adjusting, for one or more of the turbine components, the number of times the component may be replaced within the remaining lifetime of the turbine. Step (i) may also include making an adjustment for one or more of the turbomachine components as to when the component may be replaced during the remaining life of the turbomachine. The one or more turbine components may include one or more of the following components: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, eccentric yaw drive mechanism, yaw bearing or transformer.

Optionally, said initial predefined control schedule specifies a relative change in turbine maximum power level over time.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes applying sensor data from one or more turbine sensors to one or more life usage estimation algorithms.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data from a condition monitoring system.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data obtained from wind park sensors in conjunction with a site inspection program based on data obtained from wind park sensors Data and parameters related to the design of wind power plants and wind turbines to determine the loads acting on turbine components. The sensor data may include sensor data collected before the wind turbine or wind power plant is commissioned and/or constructed.

Optionally, adjusting the parameter comprises applying an offset, amplification, de-amplification or gain factor to the control arrangement. The parameters may be adjusted until all or substantially all of the fatigue life of the most heavily loaded components is consumed within the duration of the schedule. The offset can be adjusted by equalizing the area of the curve above and below the line indicating individual turbines operating at a maximum power level set at site-specific capabilities over the expected lifetime caused by fatigue damage. The offset may be adjusted until the fatigue damage over time due to operating the turbine according to the control arrangement equals the fatigue damage over time due to operating the turbine according to a constant maximum power level, the The constant maximum power level is set at the individual turbine maximum power level for the target minimum life.

Optionally, said initial predefined control arrangement specifies a gradient of the maximum power level over time. Adjusting the parameter may then include adjusting the gradient.

Optionally, the control arrangement indicates an amount of fatigue damage that should be induced over time, the method further comprising operating the wind turbine based on the one or more LUEs to induce fatigue damage at a rate indicated by the control arrangement.

Optionally, the method further comprises providing the determined control schedule to a wind turbine controller to control the power output of the wind turbine.

The method may be performed only once, or may be performed aperiodically as required. Alternatively, the method can be repeated periodically. In particular, the method can be repeated daily, monthly or annually.

A corresponding controller for a wind turbine or wind power plant configured to perform the methods described herein may be provided.

Still according to the first aspect, there is provided a method for generating a control schedule for a wind park comprising two or more wind turbines, the control schedule indicating for each wind turbine how the maximum power level increases over time Over time, the methods include:

receiving an input indicative of a target minimum life expectancy for each turbine;

determining a value indicative of a current remaining fatigue life of each of the wind turbines or one or more turbine components of each of the wind turbines based on the measured wind turbine site and/or operational data;

The parameters of the initial predefined control arrangement specifying how the maximum power level of the power plant changes over time are varied by:

i) adjust the parameters of the initial predefined control arrangement;

ii) Estimate the future fatigue life consumed by the turbine or one or more turbine components for the duration of the changed control arrangement based on the changed control arrangement using a site inspection program based on data obtained by sensors and parameters related to the design of wind power plants and wind turbines to determine the loads acting on turbine components and including the interaction between the turbines of the wind power plant; and

iii) Repeating steps (i) and (ii) until the estimated future fatigue life consumed by each of the one or more turbine components or the wind turbine is sufficient to allow the target minimum wind turbine life to be reached.

Optionally, said sensor data comprises sensor data collected prior to commissioning and/or construction of the wind turbine or wind power plant.

Optionally, step (iii) is further constrained so that for any given time period within the schedule, when the powers of all turbines are added together, the sum of the powers does not exceed The amount of power that can be carried in a connection to the grid.

According to a second aspect of the present invention there is provided a method of generating a control schedule for a wind turbine indicating how the maximum power level of the turbine varies over time, the method comprising:

receiving input indicating a maximum number of times each of the one or more turbine components is to be replaced within the remaining life of the turbine;

determining a value indicative of a current remaining fatigue life of one or more of the turbine components or the turbine based on the measured wind turbine site and/or operational data;

The parameters of the initial predefined control arrangement specifying how the maximum power level of the turbine varies over time are varied by:

iv) adjust the parameters of the initial predefined control arrangement;

v) based on the changed control arrangement and taking into account the replacement of one or more turbine components,

estimating future fatigue life consumed by the turbine or one or more turbine components for the duration of said altered control arrangement; and

vi) Repeating steps (i) and (ii) until the estimated future fatigue life consumed by each of the one or more turbine components or the wind turbine is sufficient to allow a target minimum wind turbine lifetime to be reached.

The parameters may be varied until the estimated future fatigue life consumed by the heaviest loaded components is sufficient to allow just reaching the target minimum wind turbine life, or in other words until the total fatigue life consumed is substantially the same as the target minimum wind turbine life until. This may be achieved based on a predetermined margin of the target minimum wind turbine lifetime (eg, within 0 to 1 month, within 0 to 3 months, within 0 to 6 months, or within 0 to 12 months of the target).

Optionally, step (iii) also entails maximizing energy capture over the lifetime of the turbine.

Optionally, the control arrangement is indicative of an amount by which the wind turbine may be overrated to power above its rated power.

Optionally, step (i) may also include adjusting, for one or more of the turbine components, the number of times the component may be replaced over the remaining life of the turbine. Step (i) may also include making an adjustment for one or more of the turbomachine components as to when the component may be replaced during the remaining life of the turbomachine.

Optionally, said target minimum wind turbine lifetime is a predetermined target value corresponding to a turbine design lifetime.

Optionally, the method further comprises receiving input indicative of a user-defined target minimum wind turbine lifetime.

Optionally, said initial predefined control schedule specifies a relative change in turbine maximum power level over time.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes applying sensor data from one or more turbine sensors to one or more life usage estimation algorithms.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data from a condition monitoring system.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data obtained from wind park sensors in conjunction with a site inspection program based on data obtained from wind park sensors Data and parameters related to the design of wind power plants and wind turbines to determine the loads acting on turbine components. The sensor data may include sensor data collected before the wind turbine or wind power plant is commissioned and/or constructed.

Optionally, adjusting the parameter includes applying an offset, amplification, attenuation or gain factor to the control arrangement. The parameters may be adjusted until all or substantially all of the fatigue life of the most heavily loaded components is consumed within the duration of the schedule. The offset can be adjusted by equalizing the area of the curve above and below the line indicating individual turbines operating at a maximum power level set at site-specific capabilities over the expected lifetime caused by fatigue damage. The offset may be adjusted until the fatigue damage over time due to operating the turbine according to the control arrangement equals the fatigue damage over time due to operating the turbine according to a constant maximum power level, the The constant maximum power level is set at the individual turbine maximum power level for the target minimum life.

Optionally, said initial predefined control arrangement specifies a gradient of the maximum power level over time. Adjusting the parameter may include adjusting the gradient.

Optionally, the control arrangement indicates an amount of fatigue damage that should be induced over time, the method further comprising operating the wind turbine based on the one or more LUEs to induce fatigue damage at a rate indicated by the control arrangement.

Optionally, the method further comprises providing the determined control schedule to a wind turbine controller to control the power output of the wind turbine.

Optionally, said one or more turbine components include one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters actuator, yaw drive mechanism, yaw bearing or transformer.

The method may be performed only once, or may be performed aperiodically as required. Alternatively, the method can be repeated periodically. In particular, the method can be repeated daily, monthly or annually.

A corresponding controller for a wind turbine or wind power plant configured to perform the methods described herein may be provided

Still according to said second aspect, there is provided a method of generating a control schedule for a wind park comprising two or more wind turbines, the control schedule indicating for each wind turbine how the maximum power level varies over time Over time, the methods include:

receiving an input indicative of a maximum number of times each of the one or more turbine components of each turbine will be replaced within the remaining life of the turbine;

determining a value indicative of a current remaining fatigue life of each of the wind turbines or one or more turbine components of each of the wind turbines based on the measured wind turbine site and/or operational data;

The parameters of the initial predefined control arrangement specifying how the maximum power level of the power plant changes over time are varied by:

iv) adjust the parameters of the initial predefined control arrangement;

v) Estimate future fatigue consumed by the turbine or one or more turbine components for the duration of the changed control arrangement, based on the changed control arrangement and taking into account the replacement of one or more turbine components, using a site inspection procedure lifetime, the site inspection program determines the loads acting on turbine components and includes the interaction between the turbines of the wind park based on data obtained from wind park sensors and parameters related to wind park and wind turbine design;

as well as

vi) Repeat steps (i) and (ii) until the estimated future fatigue life consumed by each of the one or more turbine components or the wind turbine is sufficient to allow the target minimum wind turbine life to be reached.

Optionally, said sensor data comprises sensor data collected prior to commissioning and/or construction of the wind turbine or wind power plant.

Optionally, step (iii) is further constrained so that for any given time period within the schedule, when the powers of all turbines are added together, the sum of the powers does not exceed The amount of power that can be carried in a connection to the grid.

According to a third aspect of the present invention there is provided a method of generating a control schedule for a wind turbine indicating how the maximum power level of the turbine varies over time, the method comprising:

determining a value indicative of a current remaining fatigue life of the turbine or one or more turbine components based on measured wind turbine site and/or operational data;

applying an optimization function that changes the initial control schedule to determine an optimized control arrangements, said optimization comprising:

estimating future fatigue life consumed by the turbine or turbine components for the duration of the altered control arrangement based on the current remaining fatigue life and the altered control arrangement; and constraining optimization of the control arrangement according to one or more input constraints ;

Wherein the input constraints include a maximum number of allowable component replacements for one or more turbine components, and the optimization further includes changing an initial value of the wind turbine lifetime to determine a target wind turbine lifetime.

According to a fourth aspect of the present invention there is provided a method of generating a control schedule for a wind turbine indicating how the maximum power level of the turbine varies over time, the method comprising:

determining a value indicative of a current remaining fatigue life of the turbine or one or more turbine components based on measured wind turbine site and/or operational data;

applying an optimization function that changes the initial control schedule to determine an optimized control arrangements, said optimization comprising:

estimating future fatigue life consumed by the turbine or turbine components for the duration of the changed control schedule based on the current remaining fatigue life and the changed control schedule; and constraining the control schedule according to one or more input constraints Optimization;

Wherein said input constraints include a target minimum wind turbine lifetime, and said optimization further includes varying an initial value of the number of component replacements to be performed on one or more components during said scheduled schedule to determine the duration of component replacement maximum number of times.

According to a fifth aspect of the present invention there is provided a method of generating a control schedule for a wind turbine indicating how the maximum power level of the turbine varies over time, the method comprising:

determining a value indicative of a current remaining fatigue life of the wind turbine or one or more turbine components based on measured wind turbine site and/or operational data;

applying an optimization function that changes the initial control schedule to determine an optimized control arrangements, said optimization comprising:

estimating future fatigue life consumed by the turbine or turbine components for the duration of the changed control schedule based on the current remaining fatigue life and the changed control schedule; and constraining the control schedule according to one or more input constraints Optimization;

Wherein, said optimizing further includes changing an initial value of the lifetime of the wind turbine, and changing an initial value of the number of component replacements to be performed on one or more components during said schedule to determine the value of one or more turbine components Combination of number of component replacements and target minimum wind turbine lifetime.

The following optional features may be applied to said third, fourth or fifth aspect.

The control arrangement can be applied throughout the lifetime of the turbine.

Optionally, the method further comprises optimizing the control schedule by varying the timing of component replacement and varying the number of component replacements until a maximum number is reached.

Optionally, the one or more turbine components that can be replaced include one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters actuator, yaw drive mechanism, yaw bearing or transformer.

Optionally, the initial control arrangement specifies a relative variation over time of a maximum attainable turbine power level at which the turbine can be operated.

Optionally, the input constraints also include an upper limit maximum power output of the turbine and/or a minimum power output of the turbine allowed by the design of the turbine.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes applying sensor data from one or more turbine sensors to one or more life usage estimation algorithms.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data from a condition monitoring system.

Optionally, determining a value indicative of the current remaining fatigue life of the turbine or one or more turbine components includes using data obtained from wind plant sensors in conjunction with a site inspection program based on the wind plant sensors and with the wind plant and Parameters relevant to wind turbine design to determine the loads acting on turbine components.

Optionally, said optimization of control arrangements includes changing control arrangements to minimize the levelized cost of energy (LCoE). The LCoE can be determined using an LCoE model that includes parameters for one or more of the following: a capacity factor that indicates the energy generated over a period of time divided by the energy that would be generated; availability, which indicates how long a turbine would be available to generate electricity; and field efficiency, which indicates the energy generated over a period of time divided by the energy. The model may also include parameters for one or more of: costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components , and transportation costs to transport refurbished or replaced components to the power plant; and service costs associated with the replacement of worn parts.

Optionally, the optimized control arrangement is an arrangement for an achievable maximum power level at which the turbine can operate, and which may specify a maximum power level higher than the rated power of said wind turbine. Alternatively, the control arrangement may specify an amount of fatigue damage that should be induced over time, the method further comprising operating the wind turbine based on the one or more LUEs to induce fatigue damage at the rate indicated by the control arrangement.

The control arrangement may dictate how the maximum power level of the turbine varies over the lifetime of the turbine.

Optionally, the method may further comprise providing the optimized control arrangement to a wind turbine controller or wind park controller to control the power output of the wind turbine.

Optionally, the method is repeated periodically. The method can be repeated daily, monthly or annually.

There may be provided a corresponding controller for a wind turbine or wind power plant configured to perform a method according to the third, fourth or fifth aspect described herein.

According to a third aspect, there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the optimizer comprising:

an optimization module configured to receive: initial values for a set of variables that are operational variables of the wind turbine and include an initial control arrangement; one or more constraints; and an indication of a current Data on remaining fatigue life;

Wherein, the optimization module is configured as:

The results received at the optimization module are determined by changing one or more of the set of variables from their initial values according to the remaining fatigue life of the turbine or one or more turbine components and the one or more constraints. optimizing the control arrangement by minimizing or maximizing an operating parameter dependent on said variable; and

Output optimized control arrangements;

Wherein the constraints include a maximum number of allowable component replacements for one or more turbine components, and the optimization module is further configured to vary an initial value of the wind turbine lifetime to determine a target wind turbine lifetime.

According to a fourth aspect there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the optimizer comprising:

an optimization module configured to receive: initial values for a set of variables that are operational variables of the wind turbine and include an initial control arrangement; one or more constraints; and an indication of a current Data on remaining fatigue life;

Wherein, the optimization module is configured as:

The results received at the optimization module are determined by changing one or more of the set of variables from their initial values according to the remaining fatigue life of the turbine or one or more turbine components and the one or more constraints. optimizing the control arrangement by minimizing or maximizing an operating parameter dependent on said variable; and

output the optimized control arrangement,

Wherein the constraints include a target minimum wind turbine lifetime, and the optimization module is further configured to vary an initial value of the number of component replacements to be performed on one or more components during the schedule to determine a component The maximum number of replacements.

According to a fifth aspect, there is provided an optimizer for generating a control schedule for a wind turbine, the control schedule indicating how the maximum power level of the turbine varies over time, the optimizer comprising:

an optimization module configured to receive: initial values for a set of variables that are operational variables of the wind turbine and include an initial control arrangement; one or more constraints; and an indication of a current Data on remaining fatigue life;

Wherein, the optimization module is configured as:

The results received at the optimization module are determined by changing one or more of the set of variables from their initial values according to the remaining fatigue life of the turbine or one or more turbine components and the one or more constraints. optimizing the control arrangement by minimizing or maximizing an operating parameter dependent on said variable; and

output the optimized control arrangement,

Wherein, the optimization module is further configured to change an initial value of the lifetime of the wind turbine, and change an initial value of the number of component replacements to be performed on one or more components during the schedule to determine one or more A combination of the number of parts replacements for the turbine components and the target minimum wind turbine lifetime.

The following optional features may be applied to the optimizer of the third, fourth or fifth aspects.

Optionally, the initial control arrangement specifies a relative variation over time of a maximum attainable turbine power level at which the turbine can be operated.

Optionally, the optimizer further includes an initialization module configured to receive initial values of the set of variables and sensor data, the initialization module configured to calculate initial values of the operating parameters.

Optionally, said one or more turbine components are one or more of the following: blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters actuator, yaw drive mechanism, yaw bearing or transformer.

Optionally, the operating parameter is the levelized cost of energy (LCoE) of the turbine, and optimizing the control arrangement includes minimizing the levelized cost of energy (LCoE). The LCoE can be determined using an LCoE model that includes parameters for one or more of the following: a capacity factor that indicates the energy generated over a period of time divided by the energy that would be generated; availability, which indicates how long a turbine would be available to generate electricity; and field efficiency, which indicates the energy generated over a period of time divided by the energy. The model may also include parameters for one or more of: costs associated with replacing one or more components, including turbine downtime, labor and equipment for component replacement, manufacturing or refurbishment costs for replacement components , and transportation costs to transport refurbished or replaced components to the power plant; and service costs associated with the replacement of worn parts.

There may be provided a controller comprising an optimizer according to any of the third, fourth or fifth aspects.

According to a third aspect there is provided a method of generating a control arrangement for a wind park comprising a plurality of wind turbines, the control arrangement indicating for each wind turbine how the maximum power level varies over time, the Methods include:

determining a value indicative of a current remaining fatigue life of each of the turbines or one or more turbine components of each of the turbines based on the measured wind turbine site and/or operational data;

applying an optimization function by varying the trade-off between energy capture and fatigue life consumed by each of the turbines or one or more turbine components of each of the turbines until an optimized control arrangement is determined, to change the initial control schedule for each of the turbines to determine an optimized control schedule, the optimization comprising:

The future fatigue life consumed by the turbine or turbine components for the duration of the changed control schedule is estimated based on the current remaining fatigue life and the changed control schedule using a site check procedure based on data obtained by sensors and parameters related to the design of the wind park and wind turbines determine the loads acting on turbine components and include the interaction between the turbines of the wind park; and

constraining the optimization of the control arrangement according to one or more input constraints;

Wherein, the constraints include a maximum number of allowable component replacements for each of the one or more turbine components of each of the wind turbines, and the optimization module is further configured to vary an initial value of the wind turbine lifetime to determine Target wind turbine lifetime.

According to a fourth aspect there is provided a method of generating a control arrangement for a wind park comprising a plurality of wind turbines, the control arrangement indicating for each wind turbine how the maximum power level varies over time, the Methods include:

determining a value indicative of a current remaining fatigue life of each of the turbines or one or more turbine components of each of the turbines based on the measured wind turbine site and/or operational data;

applying an optimization function by varying the trade-off between energy capture and fatigue life consumed by each of the turbines or one or more turbine components of each of the turbines until an optimized control arrangement is determined, to change the initial control schedule for each of the turbines to determine an optimized control schedule, the optimization comprising:

The future fatigue life consumed by the turbine or turbine components for the duration of the changed control schedule is estimated based on the current remaining fatigue life and the changed control schedule using a site check procedure based on data obtained by sensors and parameters related to the design of the wind park and wind turbines determine the loads acting on turbine components and include the interaction between the turbines of the wind park; and

constraining the optimization of the control arrangement according to one or more input constraints;

wherein the constraints include a target minimum wind turbine lifetime for each of the wind turbines, and the optimization module is further configured to change one or more components to be performed on each of the wind turbines over the course of the schedule The initial value of the number of component replacements to determine the maximum number of component replacements.

According to a fifth aspect there is provided a method of generating a control arrangement for a wind park comprising a plurality of wind turbines, the control arrangement indicating for each wind turbine how the maximum power level varies over time, the Methods include:

determining a value indicative of a current remaining fatigue life of each of the turbines or one or more turbine components of each of the turbines based on the measured wind turbine site and/or operational data;

applying an optimization function by varying the trade-off between energy capture and fatigue life consumed by each of the turbines or one or more turbine components of each of the turbines until an optimized control arrangement is determined, to change the initial control schedule for each of the turbines to determine an optimized control schedule, the optimization comprising:

The future fatigue life consumed by the turbine or turbine components for the duration of the changed control schedule is estimated based on the current remaining fatigue life and the changed control schedule using a site check procedure based on data obtained by sensors and parameters related to the design of the wind park and wind turbines determine the loads acting on turbine components and include the interaction between the turbines of the wind park; and

constraining the optimization of the control arrangement according to one or more input constraints;

Wherein said optimizing further comprises changing an initial value of each of the wind turbine lifetimes, and changing an initial value of the number of component replacements to be performed on one or more components of each of the wind turbines over the course of said schedule values to determine a combination of a number of component replacements for one or more turbine components for each of the wind turbines and a target minimum wind turbine lifetime for each of the wind turbines.

The optional features described below may be applied to the power plant level method of the third, fourth or fifth aspects.

Optionally, the initial control arrangement specifies, for each turbine, a relative change over time at a maximum turbine power level at which the turbine can be operated.

Optionally, said sensor data comprises sensor data collected prior to commissioning and/or construction of the wind turbine or wind power plant.

Optionally, the optimization function for one or more of the turbine components alters the number of times a component can be replaced over the remaining life of the turbine. The optimization function may vary for one or more of the turbomachine components when a component may be replaced during the remaining life of the turbomachine.

Optionally, the method can be further constrained so that for any given time period within the schedule, when the power of all turbines is added together, the sum of the power does not exceed The amount of power that can be carried in a connection.

A corresponding wind park controller configured to perform the method of the third, fourth or fifth aspect above may be provided.

Any of the methods described herein may be embodied in software which, when executed on a processor of a controller, will cause the controller to perform the associated method.

Site inspection software referred to herein includes methods known to those skilled in the art for simulating the operation of wind turbines based on pre-construction and/or pre-commissioning sensor data and other site information (e.g., topography, etc.) A site inspection tool for the plant's operating characteristics. The site inspection tool may also use operational data from the turbine or power plant, or from similar turbines or power plants if available. Examples include the Vestas(TM) site inspection tool. DNV GL offers an alternative site inspection software package. It consists of three related programs, "WindFarmer", "WindFarmer Bladed Link" and "Bladed", which allow the user to perform a full range of performance and load calculations.

Description of drawings

The invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

Figure 1A is a schematic front view of a conventional wind turbine;

FIG. 1B is a schematic representation of a conventional wind power plant including a plurality of wind turbines;

Figure 2 is a graph showing a conventional power curve for a wind turbine;

Figure 3 is a graph showing how the power produced by a wind turbine over time may vary with the target lifetime of the turbine;

Figure 4 is a graph showing different power schedules for wind turbines in which individual maximum wind turbine power levels are varied over the lifetime of the turbines to control power output;

FIG. 5 is a graph showing exemplary variation in total lifetime fatigue accumulated between different turbine components;

Figure 6 is an example of a simplified levelized cost of energy model for a wind power plant;

7 is a block diagram of an exemplary optimizer for optimizing a wind turbine control strategy;

Figure 8 is an example of a method for determining a wind turbine type maximum power level; and

Figure 9 is a schematic illustration of a wind turbine controller arrangement.

Detailed ways

Embodiments of the present invention seek to increase the flexibility available to turbine operators when employing control methods that compromise energy capture and fatigue loads. Specifically, embodiments provide an optimization method that allows turbine operators to optimize turbine performance (eg, AEP) according to their requirements.

In order to optimize performance, there are three parameters that can be changed in the overall wind turbine control strategy. These parameters are (i) the power schedule of the wind turbine; (ii) the remaining lifetime of the wind turbine; and (iii) the number of component replacements allowed during the remaining lifetime of the wind turbine. One or more of these parameters may be varied relative to one or more of the other parameters to achieve an optimized control strategy. The parameters may also be limited by constraints.

For example, optimizations can be performed to increase the AEP of a turbine over its lifetime and improve profit margins. The turbine operator can specify one or more constraints, after which optimization can be performed. An operator may require one of a minimum wind turbine lifetime (e.g., 19 years), a maximum number of individual component replacements (e.g., replacement of a gearbox), and/or a specific power schedule, schedule curve or profile, or schedule gradient or more.

Power scheduling is a variable that is used by wind turbine controllers to trade off energy capture and fatigue loads over the remaining turbine life, eg when overrating the turbine. The additional power generated by overrating a given turbine can be controlled by specifying values for variables such as individual wind turbine maximum power levels. The maximum power level specifies the power above rated power up to which the turbine may not operate when over rated. A power schedule may specify a constant maximum power level over the life of the turbine. Alternatively, the power schedule may specify a maximum power level that varies over the life of the wind turbine so that the amount of additional power that can be generated by overrating varies over time. For example, a power plant operator may wish to generate more electricity during the early years of a wind turbine's life at the expense of increased fatigue life depletion of turbine components, since the financial value of power generation in the early years of a project is disproportionately high.

Individual wind turbine maximum power levels for a given turbine type are constrained by the ultimate load limits of the wind turbine's mechanical components and by the design limits of the electrical components, since the maximum power cannot be safely increased beyond what would subject the turbine to more than its ultimate design load limit. High mechanical load values or electrical load levels. This upper maximum power level that cannot be exceeded by an individual wind turbine maximum power level may be referred to as a "wind turbine type maximum power level" and specifies that the determined load does not exceed the design load for that type of wind turbine. maximum power level. An example of the way the maximum power level of a wind turbine type may be calculated is given in the section "Maximum power level calculation" below.

The individual wind turbine maximum power level is the power level specified in the arrangement according to an embodiment of the invention and may be simply referred to as the maximum power level. The individual wind turbine maximum power levels may be refined for each individual turbine, i.e. based on each turbine's fatigue load value, based on the location of each of the wind turbines at its specific location or in the wind park. One or more of the conditions faced, wherein individual wind turbine maximum power levels are determined for each turbine in a given site. Individual wind turbine maximum power levels may be set such that the rate at which fatigue life is consumed by the turbine or by individual turbine components gives a fatigue life that corresponds to or exceeds a specific target life.

The remaining life of the wind turbine specifies the amount of operating life that the operator is willing to accept in order to optimize the AEP. The remaining life will depend on the point in time from the first start-up when the AEP optimization method is implemented, as the available remaining life decreases as the turbine operates.

The number of component replacements allowed during the remaining lifetime of the wind turbine can also be used to optimize the AEP. Since turbine components fatigue at different rates under different conditions, the actual lifetime of some components may well exceed the 20-year expected lifetime of a wind turbine, or equivalently the components could be overrated for a given lifetime by a greater amount. Components with longer life do not contribute to overall turbine life and have idle production capacity. However, those components with a shorter life may have a limiting effect on overrating, and AEP may be increased by replacing one or more of these components during the life of the turbine. In particular, overrating through increased torque has a particularly large impact on the fatigue life of gearboxes, generators and power take-off components. Conversely, in the case of overrating by increasing the rotor speed, the fatigue life of the blades and structural components is more heavily affected.

Replaceable components in the context of embodiments of the present invention are considered major components, eg components each accounting for 5% or more of the total wind turbine cost and which can be replaced in the field. General wear parts, which only constitute a small fraction of the overall cost of the wind turbine, are not considered. Specifically, components considered for replacement may include blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, yaw drive mechanisms, yaw bearings, or transformers. one or more.

Figure 3 shows a first example of optimization where the power schedule is varied relative to the target lifetime of the turbine. In this example, the design life of the turbine is 20 years, and the power level is fixed for the life of the turbine. It can be seen that the amount of power produced in a given year increases with decreasing wind turbine lifetime. As turbine life decreases, the rate of depletion of the fatigue life of the turbine or turbine components may increase, allowing additional power to be generated through overrating. Optimizations may be applied depending on the turbine operator's preferences. For example, the lifetime, net present value (NPV) or net present value (NPW) of the turbine that maximizes AEP may be determined and selected. NPV/NPW can be calculated using known methods.

Fig. 4 shows another optimization example, where the power schedule is again changed relative to the target lifetime of the turbine. In this example, the maximum power level specified by the arrangement is variable over the life of the turbine. An initial schedule may be specified, for example, a turbine operator may have a desired schedule profile to use. The schedule defines how the individual wind turbine maximum power levels vary over time, but this may be done in a relative rather than absolute manner. In this example, the desired schedule 401 is a linear schedule from the wind turbine type maximum power level P _max to the turbine type's nominal or nominal power level P _nom over a turbine lifetime of 20 years. The site-specific capabilities of individual turbines over a 20-year lifetime are shown by dashed line A for a typical exemplary site with an average annual wind speed below the turbine's design wind speed. For a particular turbine, it may not be possible to satisfy the desired arrangement 401 without exceeding the fatigue life of the turbine or certain turbine components within the lifetime of the turbine. Therefore, the schedule is adjusted until the total fatigue induced according to the power schedule does not exceed the design fatigue life of the most heavily loaded component.

This can be achieved by estimating the fatigue damage due to following the schedule over its duration (eg, up to the design life of the turbine or a user-specified life of the turbine). Induced fatigue damage can be estimated using the site inspection function and can be supplemented with LUE data, both of which account for fatigue damage induced by loads given microsite conditions. The arrangement can be adjusted until the resulting fatigue life of the heaviest loaded component is equal to the design fatigue life of that component. In other words, the schedule may be adjusted until all or substantially all of the fatigue life in the most heavily loaded components is exhausted for the duration of the schedule.

The arrangement can be adjusted by adjusting one or more parameters of the arrangement. This can include:

- impose some offset on the arrangement by adding or subtracting a value across the entire arrangement;

- applying a gain greater than or less than 1 to said arrangement;

- Any other means for making the control arrangement non-linearly ramp up or down by adjusting the relevant parameters

It functions to otherwise expand/compress the arrangement as needed or

Make it grow/shrink to change the arranged power level value.

In one example, adjustments to the schedule may be made based on equivalent plots of induced fatigue damage vs. time, or fatigue life remaining vs. time, for the most fatigued components, which may be through power schedule plots and using site inspection software to determine the fatigue damage to components that would be induced at a given power level at a particular turbine location (or turbine microsite) within a power plant. The graphs are adjusted until the areas defined by each arrangement above and below the corresponding capacity lines on the equivalent fatigue curve applicable to the expected turbine life are equal. This is achieved, for example, by equating the area of the curve above and below the line indicating the Fatigue damage caused by individual turbines. For example, it could be an equivalent line to dotted line A of Fig. 3, but indicating fatigue damage over time due to individual wind turbine maximum power. The curve can be shifted up or down by adding or subtracting an offset to the power schedule until the areas are equalized, or the curve can be enlarged or enlarged by adjusting one or more parameters of the curve. Reduced to achieve area equalization. The total fatigue life consumed by the turbine or turbine components would then amount to 20 years of operation. An exemplary arrangement is shown by a line 402 terminating in a black square i.

The site-specific capabilities of the turbine are shown by dashed line B over a 19-year lifetime for the same exemplary site. It can be seen that the capacity at a lifespan of 19 years is higher than that at a lifespan of 20 years. Thus, the resulting 19-year schedule, example given by line 403 , may have a higher initial maximum power level value P ^I _19yrs than the initial maximum power level value P ^I _20yrs of the 20-year lifetime schedule 402 . Arrangement 403 ends on year 19, which is indicated by black square ii.

In the example of FIG. 4 , the schedule adjustment is also subject to the additional constraint that the slope or gradient of the schedule should be equal to the slope or gradient of the initial schedule 401 over a 20-year lifetime. Another constraint as used in the example of FIG. 4 can also be applied, according to which the slope of the schedule is equal to the slope of the initial schedule 401 only until the nominal power level (which may be the rated power of the turbine) is reached, from which After that point keep the maximum power level at the nominal power level. Alternatively, embodiments may employ de-rating of the turbine such that the maximum power level specified by the arrangement is set at a level lower than the rated power of the turbine.

Adjustments to the schedule can be made in steps, which can be lower from P _max , or higher from P _nom , or higher from the power value of line A, until the proper schedule is reached for which the load is heaviest The fatigue life of the turbine components is sufficient to achieve the target turbine life. For example, the initial maximum power level P ^I may be increased or decreased from P _nom in steps of 1% until a suitable schedule is reached.

There are also other possibilities for optimizing the power schedule in terms of years of turbine life. For example, the schedules may all start at the same initial value (e.g., P _max ) and vary the gradient until defined by each schedule above the corresponding capability line on the equivalent fatigue curve applicable to the desired turbine life and The following areas are equal.

Another line 404 shows an example of an arrangement that a turbine can achieve over a 20-year lifetime, taking into account one or more component replacements. Arrangement 404 terminates in black box i. One or more components may be particularly susceptible to fatigue damage caused by overrating. For example, as shown in Figure 5, after 20 years of operation, one component may reach the 20-year life fatigue limit while other components still have some life left. In this case, replacing one or more components that caused a higher rate of fatigue damage would allow the AEP to be increased. Considered over the lifetime of the turbine, and taking into account the total cost of replacement, this can still add to the turbine's profit margin when calculating NPV.

As an alternative to specifying a schedule for a maximum power level value, it is also possible to specify a schedule for fatigue damage or remaining fatigue life, since the rate of fatigue damage induced is related to the maximum power level setting of the turbine. Then, the turbine power output is controlled to maintain the remaining fatigue life at the remaining fatigue life specified by the schedule, for example by using LUE in the turbine controller to track the fatigue life. As another alternative, an energy schedule can also be used, as it still indicates how the maximum turbine power level varies over time. Energy scheduling may be done annually, per calendar month, or at a similar time.

For the avoidance of doubt, the arrangement may also have a non-linear shape, for example a shape following a polynomial curve.

Although the schedules are shown as varying continuously over their duration, they may also vary in a stepwise manner, specifying a given maximum power level over a specific period of time, such as a month, quarter or year. A schedule may, for example, be a series of annual values over the lifetime of the turbine.

The schedule can be calculated once, or it can be calculated repeatedly at certain intervals. For example, the schedule may be calculated monthly or annually. For schedules specifying maximum power levels on an annual basis, it may be advantageous to calculate the schedule, eg, monthly or weekly, because changes to the schedule can alert the user to parameters that are changing more quickly than expected.

If the schedule is calculated once, this calculation can take place before the commissioning of the wind power plant, or at any time after commissioning. For calculations that repeat at regular intervals, the first calculation can occur before the wind power plant is commissioned, or it can occur at any time after commissioning.

first example

According to a first example, a control arrangement is generated that can be used to control a wind turbine. Relative arrangements may be defined, and one or more of a minimum wind turbine lifetime or a maximum number of major component replacements may be defined. Thereafter, the schedule is adjusted to ensure that the fatigue life of the turbine meets the target life while maximizing the AEP.

The wind turbine is operated according to one of the over-rating control techniques described herein using an over-rating controller, which may be implemented by a wind turbine controller.

A Life Use Estimator (LUE) can be used to determine and monitor the life use of components. A life service estimator can be used to ensure that the fatigue load limits of all turbine components remain within their design life. The load experienced by a given component (eg its bending moment, temperature, force or motion) can be measured and the amount of component fatigue life expended is calculated eg using techniques such as rainflow counting and Miner's law or chemical decay equations. Then, based on the lifetime usage estimator, individual turbines can be operated in a manner that does not exceed their design limits. A device, module, software component or logic used to measure the fatigue life consumed by a given turbine component may also be referred to as its Life Use Estimator and the same abbreviation (LUE) will be used to refer to the algorithm used to determine the Life Use Estimator And corresponding means, modules or software or logic components. LUE will be described in more detail below.

According to the default mode of operation, the over-rating controller will control the amount of over-rating applied based on function or schedule over the expected or certified lifetime of the wind turbine. Typically, this is 20 or 25 years.

The controller is configured to receive input parameters from, for example, a site operator, the input defining a new target lifetime for the wind turbine or one or more specific turbine components. The LUE is used to determine the lifetime use to date of a turbine or related turbine components. This places a constraint on the amount of component life remaining in the wind turbine, and thus the control arrangement. In addition, the revised target life imposes constraints on the amount of time the remaining component life must be extended.

Future available fatigue life can be calculated off-line or on-line using site inspection software and can be used to specify revised control arrangements. Site inspection functions may include calculations or one or more simulations to determine expected fatigue damage rates using site-based historical data, including site climate data measured prior to construction and/or measured after construction Site climate data, and/or data from LUE. Site climate data typically includes data from MET MAST or ground-based LIDAR, and can include wind speed, turbulence intensity, wind direction, air density, vertical wind shear, and temperature. Site check calculations can be performed remotely or as needed by the turbine/power plant level controller.

Information or parameters pertaining to a given WPP site topography, topography, wind conditions, etc. may be entered into the site inspection software. Topography and terrain information may be provided by site surveys and/or from knowledge of the WPP site, which may include details of slopes, cliffs, inflow angles of each turbine within the WPP, etc. Wind conditions, e.g., wind speed (seasonal, annual, etc.), turbulence intensity ( seasonal, annual, etc.), air density (seasonal, annual, etc.), temperature (seasonal, annual, etc.), etc.

The site inspection tool may include one or more memories, databases, or other data structures to store and maintain fatigue load values for each type of wind turbine, wind turbine type maximum power levels for each type of wind turbine, and information related to WPP site conditions. related information and/or parameters.

A revised control schedule is thus generated whereby the additional power produced through overrating is adjusted depending on whether the new target date for end of life is earlier or later than the previous target date (which could be certified life) , to expose the turbine to a higher or lower rate of fatigue damage accumulation.

The ability to make revisions to turbine control arrangements allows operators to change their priorities over time. For example, a major generator on a local grid may be taken out of operation for mid-life maintenance, or may be decommissioned entirely, and the grid may require additional support. This could be reflected in significantly higher long-term tariffs, so it would be beneficial for operators to increase energy production in the short term. Therefore, an operator may decide to reduce turbine life or reduce the life of affected components such as gearboxes and generators and generate additional power by overrating while accepting a shorter wind turbine or turbine component life .

It is possible to use methods other than LUE to determine the lifetime usage of wind turbines or turbine components. Instead, the operation of the turbine to date can be checked and the fatigue damage that has occurred so far can be calculated. This may be particularly advantageous when retrofitting over-rating controls for wind turbines, and again the site inspection software is used to calculate the future available fatigue life off-line and used to specify the maximum power level. The site check function may also include off-line or on-line calculations or one or more simulations to determine expected fatigue damage rates using site-based historical data or site data measured for the point of installation, although in this case there may be no available The calculations were performed in the absence of LUE data.

Operation of a wind turbine up to the date of assembly of an overrated controller incorporating the functionality described herein may be inspected using site inspection software to use the same parameters as the wind turbine at Measurements related to the exact location within the wind power plant site such as energy output, wind speed, wind direction, turbulence intensity, wind shear, air density, turbine mechanical load measurements ( For example, from one or more of blade load sensors), turbine electrical component temperatures and loads, icing events, component temperatures, and condition monitoring system output. These values can be used to calculate an estimate of the fatigue damage that has occurred to date on the turbine component. The future useful life of a turbine or turbine component can be calculated by applying the measured values to a site inspection functional wind turbine model or simulation based on one or more of these measurements and the wind turbine type of the turbine The value of the maximum power level is used to provide estimated fatigue damage and/or remaining fatigue life as output. The simulation or model may provide fatigue damage and/or remaining fatigue life of the turbine at the component level or as a whole. Fatigue load calculations can be performed according to various calculation procedures. Various examples of such a site inspection program will be known to those skilled in the art and will not be described in detail.

The resulting estimate of exhausted fatigue life of the turbine or turbine components may be used to determine an overrating strategy to be applied by the controller. The estimation may be used once when overrating control is initialized, which may be performed midway through the life of the turbine if the turbine is retrofitted. Alternatively, the estimation may be performed periodically during the life of the turbine, thereby periodically updating the over-rating strategy depending on how life fatigue consumption changes over the life of the turbine.

The overrating strategy is determined based on the remaining fatigue life of the wind turbine or wind turbine components, which itself is based on the operating life of the wind turbine. The amount of overrating applied is controlled so that the turbine or turbine components induce fatigue damage at a rate sufficiently low to ensure that the fatigue life of the turbine is only at, and preferably just at, the end of the intended turbine life. do.

The determination of component fatigue life estimates may be further extended or replaced by using data from one or more condition monitoring systems. A condition monitoring system (CMS) includes a number of sensors at critical points of control of the transmission system in the turbine gearbox, generator or other critical components. Condition monitoring systems provide early warning of component failures before they actually fail. Thus, the output of the condition monitoring system can be provided to a controller and can be used as an indication of the fatigue life consumed by the monitored component and in particular can provide an indication of when the fatigue life of the component has reached its end point. This provides an additional way of estimating the lifetime used.

Second example

A second example is provided to implement a more general optimization process, which can be used to implement similar kinds of optimizations as described above as well as other more general optimizations. The optimization process of the second example can be implemented by the controller applying the optimization scheme.

A complete financial cost model for the turbine, or the levelized cost of energy (LCoE) model is included and used in off-line calculations performed before installing an overrated control system, or as a wind turbine controller or wind power plant control part of the device is used online. The use of the LCoE model allows optimization of the over-rating strategy and can also take into account the replacement of major components based on their cost. As used herein, the term "levelized cost of energy" refers to a measure of the cost of energy from a turbine calculated by dividing the lifetime cost of the turbine by the lifetime energy output of the turbine.

Figure 6 shows an example of a simplified LCoE model in which various costs associated with building and operating wind turbines and wind turbine plants are considered.

The wind turbine generator (WTG) cost takes into account the total cost of manufacturing the wind turbine. Shipping costs will take into account the cost of shipping the turbine components to the installation site. Operations and maintenance (O&M) costs take into account the cost of running the turbine and can be updated as operations and maintenance occur. The service technician may provide this information to the local turbine controller, to the wind farm controller, or elsewhere. The capacity factor indicates the energy generated over a given period of time (eg, within a year) divided by the energy that would be generated if the turbine operated continuously at rated power during that period of time. Availability indicates when the turbine will be available to generate electricity. Field efficiency indicates the efficiency with which energy is extracted from the wind, which is affected by the spacing of the turbines within the field.

Only those LCoE elements that are affected by the control and component replacement strategies must be included in the LCoE model, since many parameters that can be included in the LCoE model are fixed when the turbine or wind farm is built. The affected elements are:

●Operation and maintenance (O&M) costs

ο This cost increases if more parts are replaced

●Capacity factor

ο If more aggressive overrating is used, and thus more MWh are generated, the capacity factor increases

●Availability

ο If more major components are replaced, availability is slightly reduced due to the downtime required for the replacement process

ο Slight reduction in availability if more aggressive overrating causes increased preventive replacement of worn parts or unscheduled failures

●Life

ο decreases or increases depending on constraint selection.

Including a financial cost of turbine (LCoE) model in the turbine or WPP controller allows a more flexible and efficient control strategy to be determined. For example, if conditions at a particular site are found to be particularly hostile to the gearbox, that condition will be identified and the operator can choose whether to over-rate the turbine, taking into account a certain number of gearbox replacements. The turbine controller can then determine when the gearbox should be replaced, and operate the turbine accordingly, and also optionally provide an indication when the gearbox is to be replaced.

Figure 7 shows a block diagram of an exemplary optimizer for optimizing a wind turbine control strategy that may be incorporated into a controller and may be used to implement various embodiments of the invention.

At algorithm startup, the box labeled "Initialization" is run once. This provides initial conditions for the optimization loop. Loops marked as "optimized" are executed periodically, such as once a day, once a month, or once a year. When the loop is executed, it runs as many times as necessary for the optimization process to achieve a good enough degree of convergence. Following convergence, a new set of outputs is sent to the wind turbine controller (x1) and operator (other outputs) to implement the determined control strategy. The two boxes of "Compute an estimate of LCoE" contain the same calculation method. They include all the not-yet-fixed elements of Figure 6, namely O&M cost, capacity factor, availability and lifetime. For example, tower CAPEX is already fixed and thus need not be included. However, the operation and maintenance (O&M) cost is not fixed as the gearbox may be more work intensive and is replaced once during the lifetime of the turbine, so this cost is included.

Not all connections are shown in Fig. 7, where there are many similar connections, for example between the optimization algorithm block and the "Compute LCoE estimate" block. In or with reference to Figure 7 the following symbols are used:

• N: Number of cycles (eg, years) of remaining life. If necessary, users can change it to match their desired operating strategy.

x1: 1D array of individual wind turbine maximum power levels in 1...N years, eg [3.5MW, 3.49MW, 3.48MW, 3.47MW...] for 3WM turbines

x2: a one-dimensional array of gearbox replacement times in 1...N years, such as [0,0,0,0,0,0,0,0,1,0,0,0,0,0 ]

x3: 1D array of number of generator replacements in 1...N years

x4: 1D array of main bearing replacement times in 1...N years

x5: 1D array of blade set replacement times in 1...N years

and optionally:

x6: 1D array of converter replacement times in 1...N years

x7: 1D array of pitch bearing replacement times in 1...N years

x8: 1D array of number of pitch actuator (hydraulic or electrical) replacements in 1...N years

x9: one-dimensional array of the number of yaw drive mechanism replacements in 1...N years

x10: 1D array of yaw bearing replacement times in 1...N years

x11: 1D array of transformer replacement times in 1...N years

• "_0" indicates the initial condition, eg, x1_0 is the initial condition of x1.

Referring to Figure 7, the optimization process entails determining several constants for a given turbine and computing optimized initial conditions using the values of several physical and control parameters. Once the initial conditions have been calculated, the optimization process applies functions that define the relationship between the levelized cost of energy and the input values of the physical and control parameters to determine the minimum levelized cost of energy without exceeding certain optimization constraints combination of input values.

To calculate the optimized initial conditions, several parameter values for a given turbine are determined and entered into the "Initialization" box. These values are constants for any given periodic optimization (eg, monthly). They are parameters entered by the operator and can be changed at any time, but if changed, will be applied the next time the optimization is run. These parameters may include one or more of the following: turbine/individual turbine component lifetime; gearbox replacement cost; bearing replacement cost; generator replacement cost; blade replacement cost; pitch system replacement cost; and any other necessary Part replacement costs.

For example using a site check function and/or one or more LUEs to determine the lifetime of the turbine and/or the lifetime of one or more components, or may provide the lifetime of the turbine and/or the lifetime of one or more components as constraints to be satisfied condition. Replaceable parts include blades, pitch bearings, pitch actuation systems, hubs, main shafts, main bearings, gearboxes, generators, converters, yaw drive mechanisms, yaw bearings or transformers.

Determine the total cost to replace each of said parts. For example, for a replacement gearbox, the cost will take into account whether a new or refurbished gearbox is being assembled, transportation costs, and crane and labor costs. According to the availability section in Figure 6, turbine downtime costs are also included.

Also included are other costs such as financial costs including weighted average cost of capital (WACC) and any other elements needed to calculate the impact of future turbine operating strategies on the LCoE.

Lifetime parameters may be set by the operator depending on its operating strategy for the site, or may be determined as part of optimization. Other constants are based on best knowledge, so they may be updated occasionally, but such updates will be rather infrequent. Specifically, O&M costs can only be estimated in advance, and these estimates can be replaced with actual data over time, thereby obtaining a more accurate estimate of future O&M costs.

The Initialization box and optimization algorithm use the following variables:

x1: 1D array of maximum power levels in years 1...N, eg [3.5MW, 3.49MW, 3.49MW, 3.48MW, 3.47MW...] for 3WM turbines

x2: a one-dimensional array of gearbox replacement times in 1...N years, such as [0,0,0,0,0,0,0,0,1,0,0,0,0,0 ]

x3: 1D array of number of generator replacements in 1...N years

x4: 1D array of main bearing replacement times in 1...N years

x5: 1D array of blade set replacement times in 1...N years

and optionally:

x6: 1D array of converter replacement times in 1...N years

x7: 1D array of pitch bearing replacement times in 1...N years

x8: 1D array of number of pitch actuator (hydraulic or electrical) replacements in 1...N years

x9: one-dimensional array of the number of yaw drive mechanism replacements in 1...N years

x10: 1D array of yaw bearing replacement times in 1...N years

x11: 1D array of transformer replacement times in 1...N years

The initial calculation of the estimate of LCoE uses initial estimates from the operator for initial conditions, ie, x1_0, x2_0, x_0, etc.

The signal labeled "Measurement Data" in Figure 7 contains data from sensors as well as data determined by the O&M process. The measurement data from the sensors may be from a turbine or a wind farm and may include one or more of the following options:

- one of the turbine components such as gearboxes, generators, main bearings, blades, converters, pitch bearings, pitch actuators (hydraulic or electrical), yaw drive mechanisms, yaw bearings, transformers, etc. LUE value of one or more;

- wind speed and environmental data, or other data obtained by site inspection procedures;

- CMS data for one or more of the turbine components.

Measurement data from operations and maintenance (O&M) activities includes O&M costs, which may include estimates based on costs to date, if any. This can be used with future scheduled service models, experience from other turbines of the same design at the same wind farm or other wind farms, and experience with certain components of other turbines of a different design using the same components, To give an estimate of future O&M costs in the LCoE calculation.

Depending on the initial conditions, the optimization process uses the inputs and constraints to minimize the levelized cost of energy (LCoE) by directly calculating the LCoE or by calculating certain LCoE variables. It is only necessary to calculate the portion of the LCoE that changes after the turbine is built, ie, the portion affected by O&M cost, capacity factor, availability and lifetime. The optimization is run until the LCoE is minimized, eg until the stepwise change in the calculated LCoE is within a given tolerance.

Constraints for optimization are regions that the optimization algorithm cannot enter when searching for the minimum of LCoE. Constraints may include one or more of the following: wind turbine type maximum power level; turbine type minimum power output; maximum active power capability of the wind farm's connection to the grid, i.e. the maximum of all turbine active power outputs and; and any other appropriate constraints.

Constraints can also include one or more of the following, which can be defined by the user:

- minimum or target expected wind turbine lifetime;

- the maximum number of part substitutions for all parts or one or more given parts;

- A predefined maximum power level arrangement or a predefined relative maximum power arrangement defining the shape of the maximum power arrangement.

The number of inputs per 1D array can be chosen to make the optimization algorithm's runtime more manageable. The one-dimensional arrays x1, x2, etc. are described above as being provided for each year of operation. However, it is possible to provide inputs for monthly or quarterly operations, which would provide 12 or 4 times the amount of inputs. Therefore, the annual value can be used. Of course, different time periods can be used as desired, depending on desired computation time or optimization granularity.

Again, to make runtime more manageable, wind turbine components can be selected such that only the most relevant components are used in the optimization. The selection of components to include may be based on whether component life is significantly affected by active power output above rated wind speed, which may be gearboxes, generators, main bearings and blades, among others.

Additionally or alternatively, the components used in the optimization may be selected based on their values. For example, only components having a value of 5% or higher of the total cost of the turbine are included.

The optimizer algorithm generates several outputs each time it runs to convergence. A one-dimensional array x1 representing the arrangement of the maximum power levels of the turbines in years 1...N can be used as turbine power requirements by automatically transferring said data to the wind turbine controller until the next optimization loop run (e.g. , 1 month later) and used in closed-loop control. Alternatively, the maximum power level may be used in an advisory capacity without an automatic control loop, for example by sending the maximum power level data to a computer system for output on a display for viewing by a maintenance department.

The other one-dimensional arrays x2, x3, x4 represent arrangements for component replacement. This schedule data can be exported to another computer system, allowing action to be taken. The data can be provided directly to the parts replacement scheduling software. Alternatively, component replacement data including suggested replacement dates may be used as an advisory output that is sent to a display for review by maintenance to determine manual implementation of the component replacement schedule.

It should be noted that the one-dimensional array (x1) of maximum power levels described above may be provided as overrated levels only, as overrated or derated levels, or only as derated levels to Making the maximum power level variable only requires specifying the amount over (or under) the rated power. Alternatively, the power demand may be a speed demand and/or torque demand per cycle, or may be a fatigue life consumption where power is controlled by a life usage control function as described below. The drawback of using both speed and torque demands is that the calculation time for calculating the optimal configuration will be longer.

Although the optimizer has been described above as being executed periodically, it may also be used occasionally, or even only once. For example, the optimization can be performed offline at the point where the overrated controller is installed. Alternatively, the optimizer may be embodied in a controller at the wind turbine, at the wind park or elsewhere, in which case it will be executed at certain time steps.

As mentioned above, optimization can be performed with or without LUE, as site data can be used to determine component fatigue, and thus give an indication of the usable remaining life of the turbine or turbine components.

Although the optimization algorithm is primarily described in connection with use with an overrated controller, this is not required. Optimization can be applied in conjunction with any control action that trades off energy capture and turbine fatigue loads. This may include one or more of: changing power requirements, such as by de-rating; Avoid high thrust loads; or any other control feature that compromises energy capture and fatigue loads.

Although the required calculations can be implemented anywhere, in practice strategic actions such as these are best implemented in the wind power plant controller, for example in a SCADA server. This allows service data to be entered directly on the site, avoiding communication problems from the site to the control center. However, the calculations can also be performed at the control center. The same applies to the other methods described herein, including the method in the first example.

Maximum Power Level Calculation

Exemplary techniques for determining the maximum power level that may be applied to the turbine will be described next.

A method for determining a wind turbine type maximum power level for a type of wind turbine may include simulating a load spectrum for two or more test power levels to determine the type for each of the two or more power levels. the loads on the wind turbine for each test power level; comparing the load determined for each test power level with the design load for the type of wind turbine; and setting the wind turbine type maximum power level for the type of wind turbine such that the determined The maximum test power level for which the load does not exceed the design load for that type of wind turbine.

Accordingly, a wind turbine type maximum power level can be determined for one or more types of wind turbines.

Figure 8 shows a flowchart detailing an example of setting a turbine maximum power level that may be used in conjunction with any of the embodiments. In step 301 , a check is performed to determine wind turbine mechanical component design limits for one or more types of wind turbines. In this example, design limits are determined using an off-line computer system. However, it should be appreciated that the functions described may be implemented by an online computer system or any other software and/or hardware associated with the wind turbine and/or WPP.

A wind turbine type maximum power level is the maximum power that a wind turbine of a given type is allowed to produce when the wind is reasonably high if the wind turbine is to operate at the limits of the design loads of the components of the wind turbine Level. The wind turbine type maximum power level effectively applies for the design life of the turbine. Accordingly, the wind turbine type maximum power level will generally be higher than the nominal nameplate rating for that type of wind turbine since the nominal nameplate rating is generally a more conservative value.

As used in the examples and embodiments below, "one type of wind turbine" may be understood as a wind turbine having the same electrical system, mechanical system, generator, gearbox, turbine blades, turbine blade length, hub height, etc. Accordingly, for the purposes of the embodiments of the present invention, any difference in the main structure or components of a wind turbine will effectively result in a new type of wind turbine. For example, wind turbines that are identical except for hub heights (eg tower heights) will be two different types of wind turbines. Similarly, wind turbines that are identical except for the length of the turbine blades will also be considered to be different types of wind turbines. 50Hz and 60Hz wind turbines are also considered as different types of wind turbines, which are wind turbines designed for cold climates and hot climates.

Therefore, the type of wind turbine does not necessarily correspond to the International Electrotechnical Commission (IEC) category of wind turbines, since different types of wind turbines can be in the same IEC category of wind turbines, wherein each type of wind turbine can be based on the Components are designed with maximum power levels for different wind turbine types.

In the example below, the wind turbine is rated at a nominal nameplate rated power level of 1.65MW (1650KW), has a hub height of 78m, and is designed to serve in specific IEC wind class conditions.

Thereafter, the performance of wind turbine type mechanical components of the type of wind turbine may be determined by simulating the load spectrum for the first tested rated power level to identify the loads acting on the type of wind turbine for the first power level. Design limits. The loads may be mechanical loads, fatigue loads, any other loads that a wind turbine may experience, or any combination of different loads. In this example, mechanical loads are considered, however it should be realized that other loads may also be considered, such as fatigue loads. The process of simulating a load spectrum may also include or may be an extrapolation of other forms of analysis that may be used to determine the loads on this type of wind turbine.

A load spectrum typically includes a range of different test cases that can be run in a computer simulation of a wind turbine. For example, a load spectrum may include test cases for: 8 m/s wind for 10 minutes, 10 m/s wind for 10 minutes, different wind directions, different wind turbulence, start-up of wind turbines, wind turbine downtime and so on. It should be appreciated that there may be many different wind speeds, wind conditions, wind turbine operating conditions and/or fault conditions for which test cases may be run in the wind turbine simulation of the load spectrum. Test cases may include real real data or artificial data (eg, 50-year gusts as defined in standards related to wind turbines). The simulation of the load spectrum can determine the forces and loads affecting the wind turbine for all test cases in the load spectrum. The simulation can also estimate or determine the number of possible occurrences of the test case event, for example, a test case of 10 m/s wind for 10 minutes can be expected to occur 2000 times during the 20-year lifetime of the wind turbine, so it can be calculated that Fatigue on a wind turbine over the lifetime of the wind turbine. The simulation may also calculate or determine fatigue damage or loads that may be induced by various components in the wind turbine based on the determined loads affecting the wind turbine.

In this example, the first test power level may be 1700KW as it is higher than the nominal nameplate rated power level for the type of wind turbine considered in this example. The load spectrum may then be simulated for a given type of wind turbine to determine whether the type of wind turbine can be operated at the first test power level without exceeding the final design loads of the mechanical components of the type of wind turbine. If the simulations show that the type of wind turbine is capable of operating at the first test power level, the same process may be repeated for the second test power level. For example, in this example, the second test power level may be 1725KW. The load spectrum may then be simulated for a given type of wind turbine to identify whether the type of wind turbine can be operated at the second test power level without exceeding the final design loads of the mechanical components of the type of wind turbine.

The process of simulating the load spectrum for other test power levels may be performed iteratively if the final design loads of the mechanical components are not exceeded. In this example, the test power levels are incremented in 25KW steps, but it should be appreciated that the increment steps may be any step size suitable for the purpose of identifying the maximum power level for the wind turbine type, for example, 5KW, 10KW, 15KW, 20KW , 30KW, 50KW, etc., or the incremental step can increase the percentage of the test power level, for example, 1% increment, 2% increment, 5% increment, etc. Alternatively, the process starts with a high first test power level and for each iteration the test power level is decremented by an appropriate amount until a wind turbine type maximum power level is identified, i.e. the wind turbine of that type is capable of The first test power level to operate without exceeding the final design limits.

In this example, a given type of wind turbine is identified as capable of operating at other test power levels of 1750KW, 1775KW, and 1800KW before exceeding the design limits of one or more mechanical components at 1825KW. Thus, the process identifies a wind turbine type maximum power level of 1800KW for this type of turbine.

In other embodiments, the process may further iteratively increment the test power level as wind turbines of the type described do not exceed the final design load of the mechanical components at 1800KW, but exceed the final design load of the mechanical components at 1825KW Smaller increments, such as 5KW, to identify whether the wind turbine can be operated at power levels between 1800KW and 1825KW without exceeding the mechanical final design load. However, in the present example, a power level of 1800KW is taken as the wind turbine type mechanical component design limit for this type of wind turbine.

The process of determining the wind turbine type maximum power level can then be performed for any other type of wind turbine to be analyzed. In step 302 of Fig. 8, design constraints of electrical components in a wind turbine of the type in question may be considered or evaluated against previously determined wind turbine mechanical component design constraints.

In step 302, the main electrical components may be considered to ensure that the wind turbine type power level determined for the mechanical component design limits does not exceed the design limits of the main electrical components of the wind turbine of the type being analyzed. For example, main electrical components may include generators, transformers, internal cables, contactors or any other electrical components in a wind turbine of the type described.

Based on simulations and/or calculations, it is then determined whether the main electrical components are capable of operating at the wind turbine type maximum power level previously determined for the design limits of the mechanical components. For example, operation at the design limit power level of the mechanical components may increase the temperature of one or more cables inside the wind turbine and thus reduce the current carrying capacity of the cables, which is determined by the size and heat dissipation of the cable conductors definite. Therefore, the current carrying capacity will be calculated for the new temperature conditions in order to judge whether the cable can operate at power levels up to the maximum power level of the wind turbine type. Similar considerations can be considered for other electrical components, eg, temperature of the component, capacity of the component, etc., to identify whether the electrical component is capable of operating at power levels up to the design limits of the mechanical component.

If it is determined or identified that the major electrical components are capable of operating at the previously determined mechanical component design limits, then in step 303 of FIG. 8 , for the given type of wind turbine, the wind turbine type maximum power level is set or recorded as a given Types of wind turbines are designed to limit maximum power levels according to the mechanical components. However, if it is determined or identified that a major electrical component cannot operate at the previously determined mechanical component design limits, then further investigation or action may be taken to arrive at a turbine type maximum power level that coordinates both mechanical and electrical components.

Once the wind turbine type maximum power level for each type of wind turbine is determined, this parameter can be used as a constraint in the method described above to obtain an individual maximum power level for each wind turbine in the WPP (e.g. maximum over-rated power level) arrangement.

A different individual maximum power level for each wind turbine in a WPP is advantageous because conditions in a WPP may vary across a WPP site. Thus, it may be the case that a wind turbine at one location in a WPP may face different conditions than another wind turbine of the same type at a different location in the WPP. Thus, two wind turbines of the same type may require different individual maximum power levels, or the smallest individual maximum power level may be applied to all wind turbines of that type in a WPP depending on the preferred implementation. As described herein, an individual maximum power level specific to an individual wind turbine is determined as part of the determination arrangement.

over rated control

Embodiments of the present invention may be applied to wind turbines or wind power plants that are operated by applying over-rating control to determine the amount of over-rating to apply.

The over-rating control signal is generated by the over-rating controller and used by the wind turbine controller to over-rate the turbine. The control arrangement described above may be used within or in conjunction with such an over-rating controller to set an upper limit on the amount of power that can be generated by over-rating. The specific manner in which the overrated control signal is generated is immaterial to the embodiment of the invention, but an example will be given for ease of understanding.

Each wind turbine may include an over-rating controller as part of the wind turbine controller. An over-rating controller calculates an over-rating request signal that instructs the turbine to over-rate the power output by an amount higher than the rated output achieves. The controller receives data from turbine sensors, eg, pitch angle, rotor speed, power output, etc., and can send commands, eg, set points for pitch angle, rotor speed, power output, etc. The controller may also receive commands from the grid, eg, from a grid operator, to increase or decrease real or reactive power output in response to demand or faults on the grid.

Fig. 9 shows a schematic example of a turbine controller arrangement, where an overrating controller 901 generates an overrating control signal, which the wind turbine controller can use to apply overrating to the turbine. The over-rating control signal may be generated depending on the output of one or more sensors 902/904 detecting operating parameters of the turbine and/or local conditions (eg, wind speed and direction). Overrating controller 901 includes one or more functional control modules that may be used in various aspects of overrating control. Additional functional modules can be provided, the functions of the modules can be combined, and some modules can also be omitted.

Values for individual turbine maximum power levels are provided by optimizer 907 according to a schedule determined as described herein. It provides the maximum power level at which the turbine can be operated according to the arrangement.

The additional functional modules generate the power demand and will generally act to reduce the final power demand complied with by the turbine controller. A specific example of an additional functional module is the operational constraints module 906 . Overrating takes advantage of the gap that usually exists between the design loads of the components and the loads experienced by each turbine in operation, usually better than the IEC standard simulation conditions for calculating the design loads. Overrating increases the power demand on the turbine at high winds until the operating limits specified by operating constraints (temperature, etc.) are reached, or until a power cap is reached to avoid exceeding component design loads. Operating constraints enforced by the operating constraints control module 906 limit possible over-rating power requirements as a function of various operating parameters. For example, where the protection function is ready to initiate a shutdown when the gearbox oil temperature exceeds 65°C, the operating constraints may specify that, for temperatures above 60°C, the maximum possible overrated setpoint signal as a function of the gearbox oil temperature Instead, it decreases linearly, so that "overrating is not possible" (ie, a power setpoint signal equal to rated power) is reached at 65°C.

The maximum power levels and power requirements from the functional blocks are provided to a minimization function, block 908, and a minimum value is selected. Another minimization block 909 may be provided which selects the minimum power requirement from the over-rating controller 901 and any other turbine power requirements, such as those specified by the grid operator, to produce the final power applied by the wind turbine controller need.

Alternatively, for example, the overrating controller may be part of the PPC controller 130 of FIG. 1B . The PPC controller communicates with each of the turbines and is capable of receiving data from the turbines, such as pitch angle, rotor speed, power output, etc., and sending commands to individual turbines, such as pitch angle, rotor speed, power output, etc. set point. The PPC 130 also receives commands from the grid, eg, from a grid operator, to increase or decrease real or reactive power output in response to demand or faults on the grid. The controller of each wind turbine communicates with the PPC 130 .

The PPC controller 130 receives power output data from each of the turbines and thus knows the power output of each turbine as well as the power output of the power plant as a whole at the grid connection point 140 . If desired, the PPC controller 130 may receive an operational setpoint for the power output of the power plant as a whole and divide it among each turbine so that the output does not exceed the operator assigned setpoint. The plant set point can be anywhere from 0 up to the rated power output of the plant. The "rated power" output of a power plant is the sum of the rated power outputs of the individual turbines in the power plant. The plant set point may be higher than the rated power output of the plant, ie the entire plant is overrated.

The PPC may receive input directly from the grid connection, or it may receive a signal that is a measure of the difference between the total power plant output and the nominal or rated power plant output. The differences can be used to provide a basis for overrating of individual turbines. In theory, only a single turbine could be overrated, but it is preferable to overrate multiple turbines, and most preferably to send an overrating signal to all turbines. The over-rating signal sent to each turbine may not be a fixed control, but instead may be an indication of the maximum amount of over-rating each turbine can perform. Each turbine may have an associated controller, which may be implemented within the turbine controller, or may be implemented centrally, for example, at the PPC, which will implement the functions shown in FIG. 9 One or more of these to determine whether the turbine is capable of responding to the over-rating signal, and if so, by how much. For example, if a controller within a turbine controller determines that conditions at a given turbine are favorable and above rated wind speed, it may respond aggressively and over-rate the given turbine. As the controller implements the over-rating signal, the output of the power plant will increase.

Thus, an overrating signal is generated either centrally or at each individual turbine, which signal indicates the amount by which one or more turbines or the turbines of the power plant as a whole can perform.

Lifetime Usage Estimator

As described above, embodiments of the present invention utilize a Lifetime Usage Estimator (LUE). The lifetime usage estimator will now be described in more detail. Algorithms needed to estimate lifetime usage will vary between components, and LUE may include a library of LUE algorithms that include some or all of the following: Load Duration, Load RPM Distribution, Rainflow Count, Stress Cycling Damage, Temperature Cycling Damage , generator thermal response rate, transformer thermal response rate and bearing wear. Alternatively other algorithms may be used. As mentioned above, lifetime usage estimation can be used only for selected critical components, and the use of an algorithm library enables selection of a new component for LUE and setting the appropriate algorithm and specific parameters selected from the library for that component.

In one embodiment, LUE is implemented for all major components of the turbine, including: blades; pitch bearings; pitch actuators or drive mechanisms; hubs; main shafts; main bearing housings; main bearings; gearboxes Bearings; gear teeth; generators; generator bearings; converters; generator terminal box cables; yaw drive mechanisms; yaw bearings; towers; offshore support structures (if present); foundations; and transformer windings. Alternatively, one or more of these LUEs may be selected.

As examples of suitable algorithms, rainflow counting can be used in blade structures, blade bolts, pitch systems, main shaft systems, transducers, yaw systems, towers and foundation estimators. In the blade structure algorithm, rainflow counting is applied to the blade root bending shimmy and flapping moments to identify the stress cycle range and average, and the output is sent to the stress cycle damage algorithm. For blade bolts, rainflow counting is applied to bolt bending moments to identify stress cycle ranges and averages, and the output is sent to a stress cycle damage algorithm. In pitch systems, spindle systems, towers, and foundation estimators, the rainflow counting algorithm is also applied to identify stress cycle ranges and averages, and the output is sent to the stress cycle damage algorithm. Parameters for applying the Rainflow algorithm can include:

- pitch system - pitch force;

- spindle system - spindle torque;

- tower-tower stress;

- Foundation - Foundation stress.

In the yaw system, the rainflow algorithm is applied to the tower top torque to identify the load duration and this output is sent to the stress cycle damage algorithm. In the converter, generator power and RPM are used to infer temperature, and rainflow counting is used over that temperature to identify temperature cycles and averages.

Life usage in the blade bearings can be monitored by feeding blade shimmy loads and pitch speeds as input to a load duration algorithm or a bearing wear algorithm. For gearboxes, the load speed duration is applied to the main shaft torque to calculate the used life. For the generator, the generator RPM is used to infer the generator temperature, which is used as input to the thermal reaction rate generator algorithm. For transformers, the transformer temperature is inferred from power and ambient temperature to provide input to the transformer thermal response rate algorithm.

Where possible, it is preferred to use existing sensors to provide the input on which the algorithm operates. Thus, for example, wind turbines often directly measure the blade root bending shimmy and flapping moments required by the blade structure, blade bearings, and blade bolt estimators. For a pitch system, the pressure in the first chamber of the cylinder can be measured and the pressure in the second chamber can be deduced, enabling the calculation of the pitch force. These are examples only and other parameters required as input may be measured directly or inferred from other available sensor outputs. For some parameters it may be advantageous to use additional sensors if a value cannot be deduced with sufficient precision.

Algorithms for various types of fatigue estimation are known and can be found in the following standards and texts:

Load speed distribution and load duration:

Guidelines for the Certification of Wind Turbines, Germainischer Lloyd, Section 7.4.3.2 Fatigue Loads

rain stream:

IEC 61400-1 ‘Wind turbines–Part 1: Design requirements’, Annex G Miners summation:

IEC 61400-1 ‘Wind turbines–Part 1: Design requirements’, Annex G power law (chemical denaturation):

IEC 60076-12 ‘Power Transformers–Part 12: Loading guide for dry-type power transformers’, Section 5.

Power Plant Level Control

Any of the methods described herein may be performed at the wind park level, thereby generating a plant control arrangement comprising individual control arrangements for each wind turbine. Doing so has the benefit of allowing the interaction between turbines in a given power plant to be considered.

A change in the power demand/level of the upstream turbine or turbines affects the power output and fatigue damage accumulation rate of any turbine immediately upstream of the upstream turbine or turbines. The site inspection software includes information about the positioning of the turbines within the wind park and takes into account the relative positions of the turbines within the wind park with respect to each other. Therefore, the wake effects from the upstream turbines are taken into account in the calculations implemented by the site inspection software.

In the case of some wind power plants, the power carrying capacity of the connection from the power plant to the utility grid is less than the sum of the power generated by each turbine if all the turbines were generating power at the wind turbine type maximum power level . In this case, the control arrangement of the wind turbines or wind farm is further constrained such that for any given time period within the arrangement, when the powers of all turbines are added together, the sum of the power The amount of power that does not exceed the capacity of the connection from the power plant to the grid.

Embodiments described herein rely on the analysis of turbine properties and turbine site properties in determining a control schedule for a turbine. Various calculations, including those performed by the site inspection software, may be performed off-line in one or more different computing systems, and the resulting control arrangements provided to the wind turbine or power plant controller. Alternatively, the calculations may be performed online at the wind turbine controller or power plant controller.

The embodiments described above are non-exclusive and one or more of said features may be combined or coordinated in order to obtain improved over-rating control by setting a maximum power level for each wind turbine in a wind park, The improved over-rating control takes into account the environmental and site conditions to which the wind turbine is exposed or which affect the wind turbine.

It should be noted that embodiments of the invention can be applied to both constant and variable speed turbines. Turbines can employ active pitch control whereby power limitation above rated wind speeds is achieved by feathering, which involves rotating some or all of each blade to reduce the angle of attack. Alternatively, the turbine may employ active stall control, which achieves power limitation above rated wind speed by pitching the blades in a direction opposite to that used in active pitch control to stall them.

While embodiments of the present invention have been shown and described, it should be understood that such embodiments have been described by way of example only. Numerous changes, modifications and substitutions will occur to those skilled in the art without departing from the scope of the invention as defined by the appended claims.

Claims (36)

1. a kind of generate the method arranged for the control of wind turbine, the control arranges instruction turbine peak power water Flat how to elapse and change over time, methods described includes：

Wind turbine site and/or operation data based on measurement, it is determined that indicating the turbine or one or more whirlpools The value of the current residual fatigue life of turbine component；

Optimizing application function, it is described optimization function by change energy capture with by the turbine or one or more of It is compromise untill determining that optimized control arranges between the fatigue life that turbine components are consumed, to change initial control System is arranged to determine optimized control arrangement, and the optimization includes：

Estimated based on the control arrangement after the current residual fatigue life and change by the turbine or turbine components The following fatigue life consumed in the duration that control after the change arranges；And

The optimization that the control arranges is constrained according to one or more input constraint conditions；

Wherein, the optimization also includes the initial value for changing the wind turbine life-span, and changes in the course of the arrangement The initial value of the number for the part replacement one or more parts are performed, to determine the portion of one or more turbine components The combination of the number that part is changed and target minimum wind force turbine life.

2. according to the method for claim 1, wherein, the initial control arrangement, which specifies the turbine, to be grasped The relative change that the turbine maximum power level for making to be reached elapses over time.

3. method according to claim 1 or 2, in addition to：

Optimize the control by changing the opportunity of part replacement and changing the number of part replacement until reaching maximum times Arrange.

4. according to the method any one of claim 1,2 or 3, wherein, the one or more of turbines that can be changed Machine part includes one or more of following item：Blade, pitch, pitch actuating system, wheel hub, main shaft, base bearing, tooth Roller box, generator, converter, yaw drive mechanism, yaw or transformer.

5. according to the method described in any preceding claims, wherein, the input constraint condition also includes turbine design institute The upper limit maximum power output of the turbine allowed and/or the minimum power of the turbine export.

6. according to the method described in any preceding claims, wherein it is determined that indicating the turbine or one or more whirlpools The value of the current residual fatigue life of turbine component includes：By the sensor number from one or more turbine sensors Algorithm for estimating is used according to applied to one or more life-spans.

7. according to the method described in any preceding claims, wherein it is determined that indicating the turbine or one or more whirlpools The value of the current residual fatigue life of turbine component includes：Use the data from condition monitoring systems.

8. according to the method described in any preceding claims, wherein it is determined that indicating the turbine or one or more whirlpools The value of the current residual fatigue life of turbine component includes：Check that program is used in combination from wind-powered electricity generation field sensor with website to obtain Data, the website check program be based on the wind-powered electricity generation field sensor and with the wind power plant and the wind turbine Relevant parameter is designed to determine to act on the load on turbine components.

9. according to the method described in any preceding claims, wherein, the optimization that the control arranges includes：

Change the control to arrange, so that levelized energy cost (LCoE) minimizes.

10. according to the method for claim 9, wherein, LCoE is determined using LCoE models, the model is included for following The parameter of one or more of item：

Capacity factor measure, its indicate the energy that is generated within a period of time divided by the turbine within described a period of time with volume Determine the energy that can be generated in the case of power continuous operation；

Availability, it indicates the turbine by the time available for generation electric power；And

Field efficiency, it indicates the energy generated within a period of time divided by not done completely by upstream turbine in the turbine The energy that can be generated in the case of being operated in the wind disturbed.

11. according to the method for claim 10, wherein, the model is also included for one or more of following item Parameter：

The cost associated with changing one or more parts, including the labour of turbine downtime, part replacement and set Manufacture that is standby, changing part or trimming cost and by part that is trimmed or changing transport the transport in the power plant into This；And

The cost of serving associated with the replacing of wearing part.

12. according to the method described in any preceding claims, wherein, the optimized control arrangement is the turbine function Enough operated the arrangement of reached maximum power level.

13. the method according to any one of claim 1 to 11, wherein, the control arranges instruction to push away over time Move should caused by fatigue damage amount, methods described also include based on one or more LUE to the wind turbine carry out Operation, so as to cause fatigue damage with the speed for arranging to indicate by the control.

14. according to the method described in any preceding claims, wherein, the control, which arranges to specify, is higher than the wind turbine Rated power maximum power level.

15. according to the method described in any preceding claims, wherein, the control arranges to indicate the turbine peak power How level changes within the life-span of the turbine.

16. provide wind-force according to the method described in any preceding claims, in addition to by the optimized control arrangement Turbine controller or power plant controller, to control the power output of wind turbine.

17. according to the method described in any preceding claims, wherein, periodically repeat methods described.

18. the method according to claim 11, wherein, it is daily, monthly or annual to repeat methods described.

19. a kind of be used for wind turbine or the controller of wind power plant, it is configured as performing according in claim 1 to 18 Any one described in method.

20. a kind of be used to generate the optimizer arranged for the control of wind turbine, the control arranges instruction turbine most How higher power levels elapse and change over time, and the optimizer includes：

Optimization module, it is configured as receiving：The initial value of one group of variable, one group of variable are the behaviour of the wind turbine Make variable and arranged including initial control；One or more constraintss；And the instruction turbine or one or more The data of the current residual fatigue life of individual turbine components；

Wherein, the optimization module is configured as：

By the remanent fatigue life according to the turbine or one or more of turbine components and one Or multiple constraintss make one or more of described variable be changed from its initial value and made at the optimization module Receive depending on one group of variable operating parameter minimize or maximize, come to it is described control arrangement optimize； And

Optimized control arrangement is exported,

Wherein, the optimization module is additionally configured to change the initial value in wind turbine life-span and changed in the arrangement The initial value of the number for the part replacement that one or more parts are performed in course, to determine one or more turbine portions The combination of the number and target minimum wind force turbine life of the part replacement of part.

21. optimizer according to claim 20, wherein, the initial control arrangement, which specifies the turbine, to enter The relative change that the turbine maximum power level that row operation is reached elapses over time.

22. the optimizer according to claim 20 or 21, in addition to it is configured as receiving the initial value of one group of variable With the initialization module of the sensing data, the initialization module is configured as calculating the initial value of the operating parameter.

23. according to the optimizer described in claim 20,21 or 22, wherein, the one or more of turbines that can be changed Part is one or more of following item：Blade, pitch, pitch actuating system, wheel hub, main shaft, base bearing, gear Case, generator, converter, yaw drive mechanism, yaw or transformer.

24. the optimizer according to any one of claim 20 to 23, wherein, the operating parameter is the turbine Levelized energy cost (LCoE), and the control arrangement is optimized including making the levelized energy cost (LCoE) Minimize.

25. optimizer according to claim 24, wherein, LCoE is determined using LCoE models, the model includes being directed to The parameter of one or more of following item：

Capacity factor measure, its indicate the energy that is generated within a period of time divided by the turbine within described a period of time with volume Determine the energy that can be generated in the case of power continuous operation；

Availability, it indicates the turbine by the time available for generation electric power；And

Field efficiency, it indicates the energy generated within a period of time divided by not done completely by upstream turbine in the turbine The energy that can be generated in the case of being operated in the wind disturbed.

26. optimizer according to claim 25, wherein, the model is also included for one or more of following item Parameter：

The cost associated with changing one or more parts, including the labour of turbine downtime, part replacement and set Manufacture that is standby, changing part or trimming cost and by part that is trimmed or changing transport the transport in the power plant into This；And

The cost of serving associated with the replacing of wearing part.

27. a kind of controller, including the optimizer according to any one of claim 20 to 26.

28. a kind of wind turbine, including controller according to claim 27.

29. a kind of wind power plant, including controller according to claim 27.

30. a kind of generate the method arranged for the control of the wind power plant including multiple wind turbines, the control peace How row elapses and change for each wind turbine instruction maximum power level over time, and methods described includes：

Wind turbine site and/or operation data based on measurement determine to indicate each of described turbine or the whirlpool The value of the current residual fatigue life of one or more parts of each of turbine；

Optimizing application function, the optimization function is by changing energy capture and by each of described turbine or the whirlpool It is compromise until determining through excellent between the fatigue life that one or more of turbine components of each of turbine are consumed Untill the control of change arranges, to change the initial control arrangement of each of the turbine to determine that optimized control is pacified Row, the optimization include：

Program is checked using website, is estimated based on the control arrangement after the current residual fatigue life and change by the whirlpool It is the following fatigue life consumed in the duration that the control of turbine or turbine components after the change arranges, described Website check program based on the data obtained from wind power plant's sensor and with the wind power plant and the wind-force whirlpool Turbine designs relevant parameter to determine to act on the load of turbine components and include the turbine of the wind power plant Between interaction；And

The optimization of arrangement is controlled according to one or more input constraint constraints；

Wherein, the optimization also includes the initial value for changing each of the wind turbine life-span and change will be in the arrangement Course in the initial value of the number of part replacement that one or more parts of each of the wind turbine are performed, To determine the number of the part replacement of one or more turbine components of each of the wind turbine and the wind-force The combination of the target minimum wind force turbine life of each of turbine.

31. according to the method for claim 30, wherein, the initial control arrangement specifies described for each turbine Turbine can be operated the relative change that the turbine maximum power level reached elapses over time.

32. the method according to claim 30 or 31, wherein, the sensing data include in the wind turbine or The sensing data that wind power plant is gone into operation and/or construction is collected before.

33. the method according to any one of claim 30 to 32, wherein, the optimization function is directed to the turbine portion One or more of part changes the number that can be changed in the residual life of the turbine to part.

34. according to the method for claim 33, wherein, the optimization function for one of described turbine components or More persons make a change to that when can carry out replacing to the part during the residual life of the turbine.

35. the method according to any one of claim 30 to 34, wherein, methods described is further constrained, to cause For any preset time section in the arrangement, when the power of all turbines is all added together, power it With the amount no more than the power that can be carried in the connection from the power plant to power network.

36. a kind of power plant controller, it is configured as performing the method according to any one of claim 30 to 35.