CN120212005A - Wind turbine generator set and state monitoring method and device thereof - Google Patents
Wind turbine generator set and state monitoring method and device thereof Download PDFInfo
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- CN120212005A CN120212005A CN202311833561.5A CN202311833561A CN120212005A CN 120212005 A CN120212005 A CN 120212005A CN 202311833561 A CN202311833561 A CN 202311833561A CN 120212005 A CN120212005 A CN 120212005A
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- 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
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Abstract
本公开提供了一种风力发电机组及其状态监测方法和装置。所述状态监测方法包括：实时获取风力发电机组的电气信号，其中，所述电气信号包括所述风力发电机组的发电机的转速信号和/或所述发电机的三相电流信号；对所述电气信号进行分析，得到目标频率范围对应的幅值统计值，其中，所述目标频率范围基于所述风力发电机组的目标机械部件的机械特征频率而确定；基于所述目标频率范围对应的幅值统计值，识别所述目标机械部件的运行状态。
The present disclosure provides a wind turbine generator set and a state monitoring method and device thereof. The state monitoring method comprises: acquiring an electrical signal of the wind turbine generator set in real time, wherein the electrical signal comprises a rotation speed signal of a generator of the wind turbine generator set and/or a three-phase current signal of the generator; analyzing the electrical signal to obtain an amplitude statistical value corresponding to a target frequency range, wherein the target frequency range is determined based on a mechanical characteristic frequency of a target mechanical component of the wind turbine generator set; and identifying the operating state of the target mechanical component based on the amplitude statistical value corresponding to the target frequency range.
Description
Technical Field
The present disclosure relates generally to the field of wind power generation technology, and more particularly, to a wind turbine generator set and a state monitoring method and apparatus thereof.
Background
The operating state of the mechanical components of a wind turbine generator system (hereinafter also referred to simply as a wind turbine) is generally monitored by adding corresponding sensors (e.g., vibration sensors, temperature sensors, etc.).
However, the physical space of the arrangement positions of the measuring points of the sensor is limited, so that the fan mechanical parts needing to be monitored are difficult to effectively and comprehensively monitor. For example, it is difficult to monitor various positions of the mechanical components of the wind turbine that need to be monitored (such as vibration states of gears in the gearbox, vibration states of stator windings of the generator, etc.), so that the condition monitoring of the mechanical components of the wind turbine cannot be fully covered, and thus problem identification cannot be performed at an early stage when an abnormal condition of the mechanical components occurs.
Moreover, the manner in which the operating state of the mechanical components of the blower is monitored by adding the sensor will lead to problems of increased cost, increased volume of the mechanical components, and limited transportation and installation.
Disclosure of Invention
Exemplary embodiments of the present disclosure provide a wind power generation set and a state monitoring method and apparatus thereof, which can effectively, comprehensively and accurately monitor an operation state of a mechanical part of a wind turbine without additional sensors.
According to a first aspect of embodiments of the present disclosure, a state monitoring method for a wind turbine generator system is provided, where the state monitoring method includes obtaining an electrical signal of the wind turbine generator system in real time, where the electrical signal includes a rotational speed signal of a generator of the wind turbine generator system and/or a three-phase current signal of the generator, analyzing the electrical signal to obtain an amplitude statistic corresponding to a target frequency range, where the target frequency range is determined based on a mechanical feature frequency of a target mechanical component of the wind turbine generator system, and identifying an operation state of the target mechanical component based on the amplitude statistic corresponding to the target frequency range.
Optionally, the step of analyzing the electrical signal to obtain the amplitude statistic corresponding to the target frequency range includes performing online fast fourier transform on the electrical signal to obtain the amplitude statistic corresponding to the target frequency range.
The method comprises the steps of determining the total sampling times corresponding to the frequency according to the frequency and the sampling frequency of the electric signal, sampling the sampling values of the electric signal according to the total sampling times to obtain a plurality of sampling values corresponding to the frequency, determining real parts and imaginary parts of complex forms of the electric signal under the frequency according to the plurality of sampling values corresponding to the frequency, calculating the amplitude of the electric signal under the frequency according to the real parts and the imaginary parts, and counting the amplitude of the electric signal under each frequency in the target frequency range to obtain the amplitude statistic corresponding to the target frequency range.
Optionally, the step of identifying the operating state of the target mechanical component based on the magnitude statistic corresponding to the target frequency range includes determining a threshold range to which the magnitude statistic corresponding to the target frequency range belongs, determining the operating state of the target mechanical component as an operating state type corresponding to the threshold range to which the threshold range belongs, wherein the threshold range corresponding to each operating state type is determined based on historical operating data of the target mechanical component.
Optionally, the operation state type comprises at least one of normal operation, abnormal prompt, alarm and fault.
Optionally, the state monitoring method further comprises the step of analyzing the identification result of the operation state of the target mechanical component in the whole life cycle to obtain the operation rule information of the target mechanical component.
Optionally, the target mechanical component comprises at least one of a gearbox, a bearing, a generator, and/or the mechanical characteristic frequency comprises at least one of an engagement frequency, a resonant frequency.
According to a second aspect of embodiments of the present disclosure, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform a method of condition monitoring of a wind power generator set as described above.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device comprising a processor, a memory storing a computer program which, when executed by the processor, causes the processor to perform the method of condition monitoring of a wind power generation set as described above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a wind power plant, the controller of which comprises a processor, a memory storing a computer program which, when executed by the processor, causes the processor to perform the method of condition monitoring of a wind power plant as described above.
According to the wind generating set, the state monitoring method and the state monitoring device thereof of the exemplary embodiment of the disclosure, a mode of monitoring the running state of mechanical components (such as a gear box, a bearing, a generator and the like) through a fan electric loop is provided, and the monitoring, early warning and protection of the running state of the mechanical components are realized through the monitoring and analysis of the characteristic frequency of the mechanical components in the running process of the fan, so that on one hand, the monitoring is more effective, comprehensive and accurate, and on the other hand, additional sensors are not needed, and the requirements for the additional sensors of the fan are reduced.
In the following description, some aspects and/or advantages of the present general inventive concept will be set forth, and still others will be apparent from the following description or the practice of the present general inventive concept.
Drawings
These and/or other aspects and advantages of the present application will become more apparent and more readily appreciated from the following detailed description of the embodiments of the application, taken in conjunction with the accompanying drawings, wherein:
FIG. 1 illustrates a flowchart of a method of condition monitoring of a wind turbine generator set according to an exemplary embodiment of the present disclosure;
FIG. 2 illustrates a flowchart of a method of performing an online fast Fourier transform analysis on an electrical signal to obtain magnitude statistics corresponding to a target frequency range, according to an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a flow chart of a method of condition monitoring of a wind turbine generator set according to another exemplary embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments will be described below in order to explain the present disclosure by referring to the figures.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
It should be noted that, in this disclosure, "at least one of the items" refers to a case where three types of juxtaposition including "any one of the items", "a combination of any of the items", "an entirety of the items" are included. For example, "comprising at least one of A and B" includes the case of juxtaposition of three of (1) comprising A, (2) comprising B, and (3) comprising A and B. For example, "at least one of the first and second steps is executed", that is, three cases are shown in parallel, namely (1) execute the first step, (2) execute the second step, and (3) execute the first and second steps.
FIG. 1 illustrates a flowchart of a method of condition monitoring of a wind turbine generator set according to an exemplary embodiment of the present disclosure.
As an example, the method for monitoring the state of a wind turbine according to the exemplary embodiments of the present disclosure may be performed by an electronic device having data processing capability, for example, the electronic device may be a controller (e.g., a master control or a converter controller) of the wind turbine, or a farm-level controller, and further may be a terminal (e.g., a personal notebook, a desktop, etc.) or a server (e.g., a stand-alone server, a server cluster, a cloud platform, etc.), which the embodiments of the present disclosure are not limited.
Referring to fig. 1, in step S101, an electrical signal of a wind turbine generator is acquired in real time.
By way of example, the electrical signals may include, but are not limited to, at least one of a rotational speed signal of a generator of a wind turbine, a three-phase current signal of a generator.
As an example, the rotational speed and the three-phase current of the generator may be measured in real time by the corresponding hardware measuring device, and accordingly, step S101 may include acquiring the rotational speed signal and the three-phase current signal measured by the hardware measuring device in real time.
In step S102, the electrical signal is analyzed to obtain an amplitude statistic corresponding to the target frequency range. Namely, the corresponding amplitude statistic value of the electric signal in the target frequency range is obtained.
The target frequency range is determined based on a mechanical characteristic frequency of a target mechanical component of the wind turbine.
By way of example, the target mechanical component may include, but is not limited to, at least one of a gearbox, a bearing, a generator body.
By way of example, the mechanical characteristic frequency may include, but is not limited to, at least one of an engagement frequency, a resonance frequency.
With respect to the target frequency range being determined based on the mechanical characteristic frequency of the target mechanical component, the target frequency range may be, as an example, a frequency range that encompasses the mechanical characteristic frequency of the target mechanical component. It should be appreciated that one or more target frequency ranges may be provided for each target machine component, each target frequency range encompassing one of the machine characteristic frequencies of the target machine component. For example, one target frequency range corresponding to the gearbox may be a frequency range that encompasses the meshing frequency of the gearbox, and another target frequency range corresponding to the gearbox may be a frequency range that encompasses the resonant frequency of the gearbox.
As an example, step S102 may include performing an on-line fast fourier transform analysis on the electrical signal to obtain an amplitude statistic corresponding to the target frequency range.
It should be appreciated that other manners of analyzing the electrical signal (e.g., fast fourier transform analysis, etc.) may be performed to obtain the magnitude statistic corresponding to the target frequency range, which is not limited by the present disclosure.
By way of example, when the electrical signal includes a rotational speed signal of the generator and a three-phase current signal of the generator, an online fast fourier transform analysis may be performed on the rotational speed signal to obtain an amplitude statistic corresponding to the rotational speed signal in a target frequency range, and an online fast fourier transform analysis may be performed on the three-phase current signal to obtain an amplitude statistic corresponding to the three-phase current signal in the target frequency range. For example, the rotational speed signal can be subjected to online fast Fourier transform analysis to obtain a corresponding amplitude statistic value of the rotational speed signal in each lower target frequency range (for example, a target frequency range below 2.5 Hz), and the three-phase current signal can be subjected to online fast Fourier transform analysis to obtain a corresponding amplitude statistic value of the three-phase current signal in each higher target frequency range (for example, a target frequency range above 2.5 Hz).
As an example, an on-line fast fourier transform analysis may be performed on the electrical signal to obtain a spectrum in the range of 0 to the switching frequency (e.g., 2 kHz), and then based on the spectrum, obtain an amplitude statistic corresponding to the target frequency range. The switching frequency is the switching frequency of a three-phase power module in a converter of a wind generating set.
An exemplary embodiment of a method for performing an on-line fast fourier transform analysis on an electrical signal to obtain an amplitude statistic corresponding to a target frequency range will be described below in connection with fig. 2, which is not herein expanded.
In step S103, the operating state of the target mechanical component is identified based on the magnitude statistic corresponding to the target frequency range.
As an example, step S103 may include determining a threshold range to which the magnitude statistic corresponding to the target frequency range belongs, and determining the operation state of the target mechanical component as the operation state type corresponding to the threshold range to which the magnitude statistic belongs.
By way of example, the operating state type may include, but is not limited to, at least one of normal operation, abnormal prompts, alarms, faults.
As an example, a threshold range for each operating state type may be determined based on historical operating data of the target machine component. For example, the historical operating data for the target machine component may include at least one of, but is not limited to, an electrical signal for the wind turbine generator set when the target machine component is operating properly, and an electrical signal for the wind turbine generator set when the target machine component is malfunctioning. By analyzing these historical operating data, a threshold range for each operating state type may be determined.
By way of example, the threshold range corresponding to normal operation may be determined by analyzing an electrical signal of the wind turbine generator set when the target mechanical component is operating normally, the threshold range corresponding to a fault may be determined by analyzing an electrical signal of the wind turbine generator set when the target mechanical component is faulty, and the threshold range corresponding to an abnormal prompt and the threshold range corresponding to an alarm may be formulated based on the threshold range corresponding to normal operation and the threshold range corresponding to a fault, leaving a corresponding safety margin.
It should be appreciated that when the number of target machine components is plural, for each target machine component, the target machine component has its corresponding at least one target frequency range, and each of the at least one target frequency ranges has a respective threshold range corresponding to each operating condition type. The target frequency ranges for different target machine components may be different and the threshold ranges for each operating condition type may be different for different target frequency ranges.
Further, as an example, the state monitoring method of the wind turbine generator system according to the exemplary embodiment of the present disclosure may further include analyzing the recognition result of the operation state of the target mechanical component in the full life cycle to obtain the operation rule information of the target mechanical component. Therefore, information such as which mechanical parts are easy to cause problems, which mechanical parts are reliable, and the rule that the mechanical parts are faulty can be obtained.
In addition, because the memory of the converter controller chip is limited, the converter controller can upload the analysis data (including but not limited to specific frequency spectrums, amplitude statistics corresponding to a target frequency range and identification results of an operating state) to the fan main control at regular time (one day or one week), and the fan main control uploads the data to the field control or the cloud for long-term storage.
Fig. 2 illustrates a flowchart of a method of performing an online fast fourier transform analysis of an electrical signal to obtain magnitude statistics corresponding to a target frequency range, according to an exemplary embodiment of the present disclosure.
Referring to fig. 2, in step S201, for each frequency in the target frequency range, a total number of samplings corresponding to the frequency is determined based on the frequency and the sampling frequency of the electrical signal.
As an example, the total number of samples N for each frequency may be the sampling frequency/the frequency of the electrical signal. For example, the electrical signal may be sampled at a frequency of 4kHz, and if the frequency is 20Hz, the total number of samples N for that frequency is 4000/20=200. The sampling frequency of the electrical signal is the number of samples (e.g., rotation speed value, current value) that can be acquired from the electrical signal measured in real time in a unit time by an algorithm.
In step S202, the sampled values of the electrical signal are resampled according to the total number of samplings N corresponding to the frequency, so as to obtain N sampled values corresponding to the frequency.
The sampling value of the electric signal is an array of sampling values obtained by sampling the electric signal according to the sampling frequency, and then N sampling values are obtained by sampling the array of sampling values N times.
In step S203, the real part and the imaginary part of the electrical signal in complex form at the frequency are determined based on the N sampling values corresponding to the frequency, and the amplitude of the electrical signal at the frequency is calculated based on the real part and the imaginary part.
As an example, the real part X Re and the imaginary part X Im of the electrical signal in complex form at the frequency may be determined by the following equation, and the amplitude X amp of the electrical signal at the frequency may be calculated based on the real part X Re and the imaginary part X Im.
Wherein x dqh (0) represents the 1 st sample value of the N sample values, and x dqh (i) represents the i-1 st sample value of the N sample values.
According to the simplified online FFT analysis mode provided by the exemplary embodiment of the disclosure, the calculated amount can be effectively reduced, the calculated time can be shortened, and the real-time performance of state monitoring can be improved.
In step S204, the amplitude of the electrical signal at each frequency in the target frequency range is counted, so as to obtain an amplitude statistic corresponding to the target frequency range.
As an example, the statistical manner may be a statistical average or a statistical maximum. It should be understood that other statistical approaches may be used, and this disclosure is not limited in this regard.
The method and the device have the advantages that when the running state of the mechanical part of the fan is abnormal, the corresponding characteristic frequency of the mechanical part can be reflected in the states of rotating speed, torque and the like, the mechanical part is connected with the generator, namely, the influence caused by the characteristic frequency can be transmitted to the generator, and the generator transmits the influence of the characteristic frequency to the electric signal through electromechanical conversion, so that the electric signal and the rotating speed signal of the generator are detected and analyzed in real time through the electric loop of the fan, namely, the working state of the mechanical part can be indirectly identified, and the state of the mechanical part is monitored. After the mode is adopted, the cost of the fan is not increased, and the fan electric loop is used as a sensor to perform function expansion. After the fan monitors and analyzes the generator electric signal and the rotating speed signal in real time, the results of the real-time monitoring and analysis can be uploaded to the upper computer memory for real-time display and storage, so that the state of the mechanical component is monitored in a full life cycle through a long-period data accumulation mode, and data support is provided for the long-term operation state analysis of the mechanical component.
The present disclosure provides a scheme for monitoring the state of a fan mechanical part through a fan electric loop to solve the problem that the state of the mechanical part is monitored through the fan electric loop, which is not commonly existed in a fan system, so that the state of the fan mechanical part is monitored in real time and for a long time through the fan electric loop, and the reliable early warning and protection of the fan mechanical part are realized, the requirements for external sensors such as vibration, temperature and the like are reduced, the reliability of the operation detection of the fan mechanical part is improved, and the fan cost is reduced.
FIG. 3 illustrates a flow chart of a method of condition monitoring of a wind turbine generator set according to another exemplary embodiment of the present disclosure.
Referring to fig. 3, in step S301, it is determined whether the current transformer modulates.
When the converter is modulated, it may be determined that the wind turbine is in an operational state, so that a subsequent state monitoring step may be performed.
In step S302, three-phase currents ia, ib, ic and a rotational speed n of the generator are acquired in real time.
In step S303, the collected three-phase currents ia, ib, ic and the rotation speed n are subjected to online FFT analysis, and the magnitudes of the currents and the rotation speeds corresponding to each frequency (the analysis frequency range is determined according to the mechanical characteristic frequency range of the target mechanical component) are obtained through calculation.
In step S304, the current and the rotation speed are uploaded and stored in the corresponding amplitude values of each frequency (the storage space needs to be satisfied and can be stored for a long time).
In step S305, the amplitude protection threshold value at the mechanical characteristic frequency of the target mechanical component conducted to the motor end is divided into an abnormal prompt file, an alarm file and a fault shutdown file, and the amplitude corresponding to each frequency of the current and the rotating speed is compared with the protection threshold value in real time, so as to realize the monitoring of the state of the target mechanical component.
In addition, the change trend of the running state of the mechanical component can be judged by analyzing the amplitude information corresponding to the current and the rotating speed stored in a long period in each frequency, so that the long-term monitoring and the running rule acquisition of the mechanical component are realized.
According to the fan mechanical part working state monitoring method provided by the exemplary embodiment of the disclosure, the fan electrical loop is equivalent to the sensor, the mechanical part characteristic frequency state detection is carried out, the current fan mechanical part working state monitoring can be effectively supplemented, the requirement for an additional sensor is reduced to the greatest extent, and therefore the fan cost, the mechanical part size and the transportation and installation difficulty can be reduced. In addition, the long-term operation mechanical characteristic frequency data can be stored, so that the long-period mechanical part working state monitoring and operation rule analysis can be realized.
Exemplary embodiments of the present disclosure provide a computer readable storage medium storing a computer program, which when executed by a processor, causes the processor to perform the method of condition monitoring of a wind turbine generator set as described in the above exemplary embodiments. The computer readable storage medium is any data storage device that can store data which can be read by a computer system. Examples of a computer readable storage medium include read-only memory, random-access memory, optical read-only disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the internet via a wired or wireless transmission path).
An electronic device according to an exemplary embodiment of the present disclosure comprises a processor (not shown) and a memory (not shown), wherein the memory stores a computer program which, when executed by the processor, causes the processor to perform the method for condition monitoring of a wind turbine generator set as described in the above exemplary embodiment.
As an example, the electronic device may be an electronic device with data processing capability, for example, the electronic device may be a controller (e.g., a master controller or a converter controller) of a wind generating set, or a farm-level controller, and further may be a terminal (e.g., a personal notebook, a desktop, etc.) or a server (e.g., a stand-alone server, a server cluster, a cloud platform, etc.), which is not limited by the embodiments of the disclosure.
A wind turbine according to an exemplary embodiment of the present disclosure, the controller of which comprises a processor, a memory storing a computer program, which when executed by the processor, causes the processor to perform the method for condition monitoring of a wind turbine according to the above exemplary embodiment.
Although a few exemplary embodiments of the present disclosure have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the scope and spirit of the disclosure, the scope and spirit of which is defined in the claims and their equivalents.
Claims (10)
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311833561.5A CN120212005A (en) | 2023-12-27 | 2023-12-27 | Wind turbine generator set and state monitoring method and device thereof |
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| CN202311833561.5A CN120212005A (en) | 2023-12-27 | 2023-12-27 | Wind turbine generator set and state monitoring method and device thereof |
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| CN202311833561.5A Pending CN120212005A (en) | 2023-12-27 | 2023-12-27 | Wind turbine generator set and state monitoring method and device thereof |
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