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WO2019066934A1 - Surveillance de rendement d'éolienne - Google Patents

Surveillance de rendement d'éolienne Download PDF

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Publication number
WO2019066934A1
WO2019066934A1 PCT/US2017/054459 US2017054459W WO2019066934A1 WO 2019066934 A1 WO2019066934 A1 WO 2019066934A1 US 2017054459 W US2017054459 W US 2017054459W WO 2019066934 A1 WO2019066934 A1 WO 2019066934A1
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WO
WIPO (PCT)
Prior art keywords
wind
wind turbines
power loss
processing device
wind turbine
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2017/054459
Other languages
English (en)
Inventor
Alexis Motto
Cristovian BASDEN
Chao Yuan
Sridharan Palanivelu
Bryan GILLENWATER
Noah SCHELLENBERG
Jennifer ZELMANSKI
Akshay PATWAL
Amit Chakraborty
Michael May
Matthew Evans
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens Energy Inc
Original Assignee
Siemens Energy Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Energy Inc filed Critical Siemens Energy Inc
Priority to PCT/US2017/054459 priority Critical patent/WO2019066934A1/fr
Publication of WO2019066934A1 publication Critical patent/WO2019066934A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/028—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/335—Output power or torque
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present disclosure generally relates to energy generation performance and more particularly to wind turbine performance monitoring.
  • a wind turbine converts the kinetic energy of the wind into electrical energy.
  • Large wind turbines (sometimes referred to as utility-level wind turbines) can be used to generate power and supply it to a utility grid.
  • These utility-level wind turbines can be distributed throughout a geographic area (e.g., on-shore or off-shore) to form a group or fleet of wind turbines, also known as a wind farm.
  • a wind farm can be a significant source of renewable energy.
  • a computer-implemented method for energy loss estimation for wind turbine performance monitoring includes receiving, by a processing device, wind speed data and power generation data for each of a plurality of wind turbines.
  • the plurality of wind turbines are distributed through a geographic area.
  • the method further includes estimating, by the processing device, a potential power loss for at least one of the plurality of wind turbines based at least in part on the wind speed data and the power generation data.
  • the method further includes implementing a corrective action when it is determined that the potential power loss for the at least one of the plurality of wind turbines is greater than a threshold power loss.
  • a system for wind turbine performance monitoring includes a plurality of wind turbines, a memory having computer readable instructions, and a processing device for executing the computer readable instructions for performing a method.
  • the method includes receiving, by a processing device, wind speed data and power generation data for each of the plurality of wind turbines.
  • the plurality of wind turbines are distributed through a geographic area.
  • the method further includes estimating, by the processing device, a potential power loss for at least one of the plurality of wind turbines based at least in part on the wind speed data and the power generation data.
  • the method further includes implementing a corrective action when it is determined that the potential power loss for the at least one of the plurality of wind turbines is greater than a threshold power loss.
  • a computer program product for wind turbine performance monitoring includes a computer-readable storage medium having program instructions embodied therewith, the program instructions being executable by a virtual reality processing system to cause a processing device to perform a method.
  • the method includes receiving, by a processing device, wind speed data and power generation data for each of a plurality of wind turbines.
  • the plurality of wind turbines are distributed through a geographic area.
  • the method further includes estimating, by the processing device, a potential power loss for at least one of the plurality of wind turbines based at least in part on the wind speed data and the power generation data.
  • the method further includes implementing a corrective action when it is determined that the potential power loss for the at least one of the plurality of wind turbines is greater than a threshold power loss.
  • FIG. 1 depicts a map of a wind farm having a plurality of wind turbines, according to aspects of the present disclosure
  • FIG. 2 depicts a block diagram of a processing system for estimating energy loss for wind turbine performance monitoring, according to aspects of the present disclosure
  • FIG. 3 depicts a flow diagram of a method for estimating energy loss for wind turbine performance monitoring, according to aspects of the present disclosure
  • FIG. 4 depicts a flow diagram of another method for estimating energy loss for wind turbine performance monitoring, according to aspects of the present disclosure
  • FIG. 5 depicts graphs of wind speed weekly time series for the wind turbines of FIG. 1, according to aspects of the present disclosure
  • FIG. 6 depicts graphs of power weekly time series for the wind turbines of FIG. 1 , according to aspects of the present disclosure
  • FIG. 7 depicts graphs of power loss weekly time series for the wind turbines of FIG. 1, according to aspects of the present disclosure.
  • FIG. 8 depicts a processing system for implementing the techniques described herein according to examples of the present disclosure.
  • a fleet of similar or identical utility-scale wind turbines commissioned in a given geographical area converts mechanical energy from wind speed into electrical energy (i.e., power). Barring any binding operational or design constraints, each wind turbine operates insomuch as to efficiently harvest and convert a maximum amount of prevailing mechanical energy into electrical power. Any wind turbine whose operation deviates from the said maximum power-harvesting engineering design principle is said to be under-performing, which results in energy loss (i.e., power loss). By identifying power loss, wind turbine performance can be improved.
  • a lowercase letter denotes a vector
  • an uppercase letter denotes a matrix
  • a calligraphic symbol denotes a feasible set.
  • the symbol X denotes the feasible set of the variable x. If x is a variable, then x and denote its lower bound and upper bound, respectively.
  • the symbol denotes the index set of turbines. If x denotes a vector, the symbol n x denotes the dimension of x.
  • E [x] is used as shorthand for the expected value of x
  • y] is used as as the conditional expectation of x given y.
  • a wind turbine operates automatically, self-starting when the wind speed is above a cut-in speed (i.e., the wind speed limit below which the wind turbine does not generate power), denoted f° r turbine i at time t.
  • a cut-in speed i.e., the wind speed limit below which the wind turbine does not generate power
  • Another existing approach uses machine learning to create multi-variable models of turbine performance when sufficient information is available. For example, an ensemble of regression trees has been proposed to predict turbine real power as a function of several inputs including shear, wind speed, and hub height turbulence. These existing approaches are instances of the "random forest" approach.
  • Another existing approach uses an additive multi-variate kernel technique that can include the environmental factors listed above as a new power curve model. Note that a power curve, in this case, is a high- dimensional manifold. Multi-variable methods require the prior construction of high- dimensional power curves, which may be meaningful only after observing data over a sufficiently long period of time. In essence, they require more data and are more difficult to understand and to implement than the novel techniques described herein.
  • the present techniques overcome the problems of existing approaches for monitoring wind turbine performance by providing a data analytics model for monitoring the performance of utility-scale wind turbines.
  • the present techniques do not require the construction of any actual or statistical performance curves.
  • the present techniques also support model uncertainties, disturbances, soft operational constraints, and hard physical constraints on decision variables.
  • the present techniques provide a special- purpose algorithm to exploit the special structure of the data analytics model for monitoring the performance of utility-scale wind turbines.
  • FIG. 1 depicts a map 100 of a wind farm having a plurality of wind turbines 101, 102, 103, 104, 105 (collectively "wind turbines 101— 105"), according to aspects of the present disclosure.
  • the wind turbines 101-105 are distributed across a geographic area that experiences similar conditions.
  • Each of the wind turbines 101-105 may experience approximately equivalent environmental conditions at their individual location.
  • the ambient temperature at each of the wind turbines 101-105 may be approximately equal (e.g., with a few degrees of the same temperature).
  • the wind speed at each of the wind turbines 101-105 may be approximately equal (e.g., within a few meters per second (m/s) of the same wind speed).
  • Other environmental conditions may also be similar at each of the wind turbines 101-105.
  • the wind turbines 101-105 are distributed across a geographic area in which the wind turbines 101-105 experience similar conditions, the data collected at each of the wind turbines 101-105 can be compared and used to estimate power loss.
  • the wind turbines 101-105 are shown to be on-shore; however, in some examples, the wind turbines 101-105 can be located on-shore, off-shore, or a combination of on-shore and off-shore. Although five wind turbines are depicted in FIG. 1, other numbers of wind turbines can be implemented according to aspects of the present disclosure.
  • FIG. 2 depicts a block diagram of a processing system 200 for estimating energy loss for wind turbine performance monitoring, according to aspects of the present disclosure.
  • the processing system 200 includes, for example, a processing device 202, a memory 204, a data engine 210, a power loss estimation engine 212, and a corrective action engine 214.
  • the various controllers, engines, and/or modules of the processing system 200 can be implemented as instructions stored on a computer-readable storage medium, as hardware modules, as special-purpose hardware (e.g., application specific hardware, application specific integrated circuits (ASICs), as embedded controllers, field- programmable gate arrays (FPGAs), hardwired circuitry, etc.), or as some combination or combinations of these.
  • the controllers, engines, and/or modules described herein can be a combination of hardware and programming.
  • the programming can be processor executable instructions stored on a tangible memory, and the hardware can include the processing device 202 for executing those instructions.
  • a system memory (e.g., the memory 204) can store program instructions that when executed by the processing device 202 implement the controllers, engines, and/or modules described herein.
  • Other controllers, engines, and/or modules can also be utilized to include other features and functionality described in other examples herein.
  • FIG. 3 depicts a flow diagram of a method 300 for estimating energy loss for wind turbine performance monitoring, according to aspects of the present disclosure.
  • the method 300 can be performed by any suitable processing system, such as the processing system 200, the processing system 800 of FIG. 8, suitable combinations thereof, and/or another suitable processing system.
  • the data engine 210 can receive data from the wind turbines 101-105.
  • the wind turbines 101-105 can be equipped with a sensor, sensors, and/or sensor arrays to collect data about the wind turbines 101-105 and the environmental conditions at the wind turbines 101-105.
  • the sensors associated with the wind turbines can collect wind speed data, ambient temperate data, atmospheric pressure data, and other data about the environmental conditions around the wind turbines 101— 105.
  • the sensors can also collect data about the wind turbines 101-105, such as real power output of each wind turbine.
  • one or more of the wind turbines 101-105 can be equipped with a temperature sensor to collect temperature data, a wind sensor (e.g., an anemometer) to collect wind speed data, an atmospheric pressure sensor (e.g., a barometer) to collect atmospheric pressure data, a power sensor (e.g., a power meter) to collect power generation (i.e., real power output) data, and/or other sensors to collect other data.
  • a temperature sensor to collect temperature data
  • a wind sensor e.g., an anemometer
  • an atmospheric pressure sensor e.g., a barometer
  • a power sensor e.g., a power meter
  • other sensors can be used to collect other data.
  • potential power loss can be estimated.
  • the power loss estimation engine 212 estimates a potential power loss for one or more of the wind turbines 101-105 using the wind speed data and the power generation data.
  • the power loss estimation engine 212 estimates a potential power loss by comparing wind speed data and power generation data for one of the wind turbines (e.g., wind turbine 103) to wind speed data and power generation data for the other of the wind turbines (e.g., the wind turbines 101, 102, 104, 105).
  • Power loss is defined as the energy per unit time below a threshold at a given time t > 0.
  • Power loss can be defined mathematically using the following formula:
  • y thit is an auxiliary variable, used here for notational convenience, and defined as follows:
  • the indices can be recast in vector form for each of the turbines 101-105. Accordingly, let denote a permutation matrix such that the following condition holds true:
  • x denotes an n-dimensional vector whose components are x lt x n .
  • the condition (4) states that applying the permutation matrix P wst to the wind speed vector, observed at time t, for all yields a new vector of the observed wind speed value-sorted in ascending order.
  • the performance power index can be recast in a compact vector form as follows:
  • a corrective action can be implemented at block 306 (e.g., by the corrective action engine 214) when it is determined that the potential power loss for one of the wind turbines is greater than a threshold power loss. Examples of corrective actions can be issuing an alert to an operator, dispatching a technician to perform maintenance (e.g., troubleshoot, repair, etc.) on the wind turbine, disabling the wind turbine to prevent damage or in anticipation of maintenance, etc.
  • FIG. 4 depicts a flow diagram of another method 400 for estimating energy loss for wind turbine performance monitoring, according to aspects of the present disclosure. The method 400 can be performed by any suitable processing system, such as the processing system 200, the processing system 800 of FIG.
  • the method 400 identifies a wind turbine of interest, determines a subset of wind turbines have less wind speed than the wind turbine of interest, and compares the power generated by the wind turbine of interest to the wind turbines with the maximum power generation of the subset. The power loss for the turbine of interest can then be determined.
  • wind speed is measured (e.g., using an anemometer) at each of the wind turbines (e.g., the wind turbines 101-105) to generate wind speed data.
  • power generation is measured (e.g., using a power meter) at each of the wind turbines (e.g., the wind turbines 101-105) to generate power generation data.
  • Blocks 402 and 404 occur concurrently such that the wind speed data and the power generation data are correlated with respect to time (e.g., a wind speed data point correlates to a power generation data point at the same time t).
  • the wind speed data and the power generation data are sent to the data engine 210.
  • the wind speed data is depicted in FIG. 5, while the power generation data are depicted in FIG. 6
  • FIG. 5 depicts graphs 501, 502, 503, 504, 505 of wind speed weekly time series (collectively "wind graphs 501-505") for the wind turbines 101-105 of FIG. 1, according to aspects of the present disclosure.
  • the wind speed is expressed in meters per second (m/s or ms "1 ) over time for a period of two years (e.g., from 01/01/2013 through 12/31/2014).
  • FIG. 6 depicts graphs 601, 602, 603, 604, 605 of power weekly time series (collectively "power graphs 601-605") for the wind turbines 101-105 of FIG. 1 , according to aspects of the present disclosure.
  • the power graphs 601-605 illustrate the normalized power of each of the wind turbines 101- 105 over a period of two years (e.g., from 01/01/2013 through 12/31/2014).
  • the graph 601 corresponds to the wind turbine 101
  • the graph 602 corresponds to the wind turbine 102, etc.
  • the power generation is expressed as a ratio of the capacity of the wind turbine generator.
  • the ratio of capacity is expressed as the actual real power of a wind turbine generator divided by the capacity of the wind turbine generator. This provides a normalized power measure, which is depicted in FIG. 6.
  • i is set to 1, where i represents a wind turbine of interest.
  • Blocks 406, 408, 410, 412, 414, and 416 are now described, which can be performed by the power loss estimation engine 212.
  • a wind turbine (TB) is designated as a turbine of interest from the group of wind turbines.
  • wind turbine 101 is designated as a turbine of interest from the group of wind turbines 101-105.
  • a subset of the group of wind turbines is identified. The members of the subset are wind turbines with a wind speed less than the wind speed of the wind turbine of interest.
  • the wind turbines 102, 104, and 105 may have wind speeds less than the wind turbine 101 (i.e., the turbine of interest).
  • one of the wind turbines of the subset is identified as having the greatest power generation.
  • the wind turbine 105 may be determined to have the greatest power generation.
  • the power loss for the wind turbine of interest is determined by comparing the power generated by the wind turbine of interest against the power generated by the wind turbine of the subset with the greatest power generation.
  • the power generated by the wind turbine 101 is compared to the power generated by the wind turbine 105. To the extent that the power generated by the wind turbine of interest is less than the power generated by the wind turbine of the subset with the greatest power generation, a power loss is determined to exist.
  • FIG. 7 depicts graphs of power loss weekly time series 701, 702, 703, 704, 705 (collectively "power loss graphs 701-705") for the wind turbines 101-105 of FIG. 1, according to aspects of the present disclosure.
  • the power loss graphs 701-705 plot a performance index as power loss per kilowatt generated over time. It should be expected that under normal operating conditions, the power loss for a wind turbine at a particular time is approximately zero. When the power loss rises, such as above a particular threshold, a problem with the wind turbine may exist. For example, the wind turbine may be experiencing a mechanical problem, may not be manipulating its blades to account for changes in wind speed/direction, or may be otherwise performing sub- optimally.
  • Power loss may also be caused, for example, by a wind turbine being temporarily shut down, such as for maintenance, which may be the cause for the extreme power loss spike in power loss graph 702 around August 2014.
  • peaks of the power loss graphs 701-705 represent more significant power loses, while the valleys (i.e., plots around zero) represent less (or no) power loss.
  • the power loss threshold it is determined whether power loss exists. This may include comparing the power loss for each wind turbine to an adjustable power loss threshold to determine whether the power loss is problematic. According to examples of the present disclosure, it can be determined whether the power loss exceeds the adjustable power loss threshold. For example, if a power loss threshold is set to 0.05%, any power loss below this power loss threshold may be considered normal, while power loss in excess of this power loss threshold may be considered problematic. It should be appreciated that other power loss thresholds can be implemented and that the power loss threshold can be adjustable for different geographic environments, operating conditions, etc. and can even be adjusted on a turbine-by-turbine basis.
  • an action can be implemented. Examples of corrective actions can be issuing an alert to an operator, dispatching a technician to perform maintenance (e.g., troubleshoot, repair, etc.) on the wind turbine, disabling the wind turbine to prevent damage or in anticipation of maintenance, etc. If at decision block 420 it is determined that no power loss has occurred and/or upon the action being implemented at block 422, the method 400 can repeat or end.
  • maintenance e.g., troubleshoot, repair, etc.
  • the present techniques provide several benefits. For example, by determining potential power loss of a wind turbine, problems with the wind turbine can be identified and corrected, thereby increasing the efficiency of the wind turbine to generate power.
  • the present techniques utilize fewer data and are easier to understand and implement that existing wind turbine performance monitoring techniques. For example, no power curve needs to be constructed to predict real power as in existing techniques.
  • Example embodiments of the disclosure include or yield various technical features, technical effects, and/or improvements to technology.
  • Example embodiments of the disclosure provide for wind turbine performance monitoring by using wind speed data and power generation data to estimate a potential power loss for a wind turbine, such as by comparing the wind speed data and the power generation data for the wind turbines to the wind speed data and the power generation data for the other wind turbines in the same geographic area, without the need to construct a power curve (e.g., a statistical or actual performance curve).
  • a power curve e.g., a statistical or actual performance curve.
  • FIG. 8 illustrates a block diagram of a processing system 800 for implementing the techniques described herein.
  • processing system 800 has one or more central processing units (processors) 821a, 821b, 821c, etc. (collectively or generically referred to as processor(s) 821 and/or as processing device(s)).
  • processors 821 may include a reduced instruction set computer (RISC) microprocessor.
  • RISC reduced instruction set computer
  • processors 821 are coupled to system memory (e.g., random access memory (RAM) 824) and various other components via a system bus 833.
  • RAM random access memory
  • ROM Read only memory
  • BIOS basic input/output system
  • I/O adapter 827 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 823 and/or a tape storage drive 825 or any other similar component.
  • I/O adapter 827, hard disk 823, and tape storage device 825 are collectively referred to herein as mass storage 834.
  • Operating system 840 for execution on processing system 800 may be stored in mass storage 834.
  • a network adapter 826 interconnects system bus 833 with an outside network 836 enabling processing system 800 to communicate with other such systems.
  • a display 835 is connected to system bus 833 by display adaptor 832, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller.
  • adapters 826, 827, and/or 832 may be connected to one or more I O busses that are connected to system bus 833 via an intermediate bus bridge (not shown).
  • Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI).
  • PCI Peripheral Component Interconnect
  • Additional input/output devices are shown as connected to system bus 833 via user interface adapter 828 and display adapter 832.
  • a keyboard 829, mouse 830, and speaker 831 may be interconnected to system bus 833 via user interface adapter 828, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.
  • processing system 800 includes a graphics processing unit 837.
  • Graphics processing unit 837 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display.
  • Graphics processing unit 837 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.
  • processing system 800 includes processing capability in the form of processors 821, storage capability including system memory (e.g., RAM 824), and mass storage 834, input means such as keyboard 829 and mouse 830, and output capability including speaker 831 and display 835.
  • system memory e.g., RAM 824
  • mass storage 834 e.g., RAM 834
  • input means such as keyboard 829 and mouse 830
  • output capability including speaker 831 and display 835.
  • a portion of system memory (e.g., RAM 824) and mass storage 834 collectively store an operating system 840 to coordinate the functions of the various components shown in processing system 800.

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  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Wind Motors (AREA)

Abstract

L'invention concerne des exemples de techniques de surveillance du rendement d'une éolienne. Dans un exemple de mise en œuvre selon certains aspects de la présente invention, un procédé mis en œuvre par ordinateur, permettant de surveiller le rendement d'une éolienne, consiste à recevoir, à l'aide d'un dispositif de traitement, des données de vitesse du vent et des données de production d'énergie pour chaque éolienne parmi une pluralité d'éoliennes. La pluralité d'éoliennes sont réparties dans une zone géographique. Le procédé consiste en outre à estimer, à l'aide du dispositif de traitement, une perte d'énergie potentielle pour au moins une éolienne parmi la pluralité d'éoliennes, en fonction, au moins en partie, des données de vitesse du vent et des données de production d'énergie. Le procédé consiste en outre à mettre en œuvre une action corrective lorsqu'il a été établi que la perte d'énergie potentielle pour ladite éolienne parmi la pluralité d'éoliennes est supérieure à une perte seuil d'énergie.
PCT/US2017/054459 2017-09-29 2017-09-29 Surveillance de rendement d'éolienne Ceased WO2019066934A1 (fr)

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CN115828439A (zh) * 2021-09-17 2023-03-21 北京金风科创风电设备有限公司 风力发电机组异常损耗的识别方法及装置
CN115933503A (zh) * 2023-03-10 2023-04-07 山东盛日电力集团有限公司 一种发电设备的智能调节控制方法及系统
US20230213560A1 (en) * 2021-12-30 2023-07-06 SparkCognition, Inc. Calculating energy loss during an outage
EP4557574A4 (fr) * 2022-07-15 2025-11-05 Sustech Inc Système et programme d'exploitation

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CN115828439B (zh) * 2021-09-17 2024-02-02 北京金风科创风电设备有限公司 风力发电机组异常损耗的识别方法及装置
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