CN117167205A - Abnormality detection method of wind turbine generator pitch system, controller and wind turbine generator - Google Patents
Abnormality detection method of wind turbine generator pitch system, controller and wind turbine generator Download PDFInfo
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Abstract
An abnormality detection method of a variable pitch system of a wind turbine generator, a controller and the wind turbine generator are disclosed. The abnormality detection method includes: obtaining pitch angles of three blades at each sampling moment; the pitch angles of three blades at the same sampling moment are subjected to two-by-two difference, and the difference of the pitch angles of every two blades is obtained; calculating a numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of every two blades based on the obtained difference between the pitch angles of every two blades; and determining whether the variable pitch system of the wind turbine generator is abnormal or not based on the numerical statistic. The abnormality detection method, the controller and the wind turbine generator can accurately monitor the angle conditions of three blades of the pitch system in real time, timely detect early abnormality or failure of the pitch system, and ensure stable and safe operation of the wind turbine generator.
Description
Technical Field
The present disclosure relates generally to the field of wind power generation technologies, and more particularly, to an abnormality detection method for a pitch system of a wind turbine, a controller, and a wind turbine.
Background
Wind turbines are devices that convert wind energy into electrical energy. Wind energy is converted into electric energy through a wind driven generator of the wind turbine generator, and electric energy generated by the wind driven generator is transmitted to a power grid through grid connection control of the wind driven generator. In the wind turbine generator, the variable pitch system plays a vital role in tracking the maximum power of the wind turbine generator and ensuring the safe shutdown of the wind turbine generator.
When the pitch system is abnormal (for example, one of the most common cases is deviation of the angle of three blades), the rotor aerodynamic unbalance is caused, the rotation speed stability of the generator is affected, and certain vibration of the wind turbine generator can be caused. In addition, the three blade angle deviates, and some early failure of devices generally representing the pitch system occurs, such as early wear of a brake valve, abnormal output of a pitch drive, abnormal output of a speed command output module and the like. The blade angle deviation fault of the wind driven generator generally triggers the wind turbine generator to stop when the blade angle deviation is larger than 3.5 degrees, but more times, the blade angle deviation is not larger than the fault threshold value during the running of the wind turbine generator, so that the wind turbine generator cannot trigger the fault, but belongs to the operation with damage. As the run time of the tape wound is prolonged, the abnormality of the device is gradually severe or increased frequently.
Therefore, in the running process of the wind turbine generator, the blade angle deviation and the change condition thereof need to be detected in real time.
Disclosure of Invention
The embodiment of the disclosure provides an abnormality detection method of a variable pitch system of a wind turbine, a controller and the wind turbine, which can accurately monitor the angle conditions of three blades of the variable pitch system in real time, timely detect early abnormalities or faults of the variable pitch system, and ensure the stable and safe operation of the wind turbine.
In one general aspect, there is provided an abnormality detection method of a wind turbine pitch system, the abnormality detection method including: obtaining pitch angles of three blades at each sampling moment; the pitch angles of three blades at the same sampling moment are subjected to two-by-two difference, and the difference of the pitch angles of every two blades is obtained; calculating a numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of every two blades based on the obtained difference between the pitch angles of every two blades; and determining whether the variable pitch system of the wind turbine generator is abnormal or not based on the numerical statistic.
Optionally, the anomaly detection method further includes: calculating a numerical statistic reflecting a distribution characteristic of the difference between the pitch angles of every two blades based on the obtained difference between the pitch angles of every two blades in response to the obtained number of the difference between the pitch angles of every two blades reaching a preset number threshold; or, in response to the time period for obtaining the difference of the pitch angles of every two blades reaching a preset time threshold, calculating a numerical statistic reflecting the distribution characteristic of the difference of the pitch angles of every two blades based on the obtained difference of the pitch angles of every two blades.
Optionally, the numerical statistic includes a kurtosis value.
Optionally, based on the numerical statistic, the step of determining whether the wind turbine pitch system is abnormal comprises: and determining that the pitch system of the wind turbine generator is abnormal in response to the kurtosis value being greater than a first kurtosis threshold.
Optionally, the step of calculating a numerical statistic reflecting a distribution characteristic of the difference between the pitch angles of each two blades based on the obtained difference between the pitch angles of each two blades includes: and calculating the kurtosis value based on the acquired number of the pitch angles of every two blades, the pitch angle difference of every two blades at each sampling time, the average value and standard deviation of the pitch angle difference of every two blades.
Optionally, the anomaly detection method further includes: and carrying out frequency domain transformation on the obtained difference of the pitch angles of every two blades to obtain a frequency spectrum of the difference of the pitch angles of every two blades.
Optionally, based on the numerical statistic, the step of determining whether the wind turbine pitch system is abnormal comprises: and responding to the maximum value of the signal intensity of the frequency spectrum and the frequency corresponding to the maximum value of the signal intensity to meet a first preset condition, and responding to the kurtosis value to meet a second preset condition, and determining that the pitch system of the wind turbine generator is abnormal.
Optionally, the first preset condition includes: the maximum value of the signal intensity of the frequency spectrum is larger than a preset intensity threshold value, and the frequency corresponding to the maximum value of the signal intensity is not zero; the second preset condition includes: the kurtosis value is greater than a first kurtosis threshold.
Optionally, the anomaly detection method further includes: and triggering the wind turbine generator to stop in a fault mode in response to the kurtosis value being greater than a second kurtosis threshold, wherein the second kurtosis threshold is greater than the first kurtosis threshold.
In another general aspect, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the abnormality detection method as described above.
In another general aspect, there is provided a controller including: a processor; and a memory storing a computer program which, when executed by the processor, implements the abnormality detection method as described above.
In another general aspect, a wind turbine is provided, the wind turbine comprising a controller as described above.
According to the abnormality detection method, the controller and the wind turbine, which are disclosed by the embodiment of the application, the wind turbine variable pitch system does not need to be provided with a blade angle deviation detection threshold value and a detection window, and the detection result can embody the change rule of the blade angle deviation and the duration time of the change rule, so that the abnormality detection method, the controller and the wind turbine can be directly suitable for detection of different working conditions, different wind conditions and different wind turbines. Meanwhile, the detection result is insensitive to the instantaneous change of the angle deviation of the blade, and false detection cannot be caused by angle jump, so that the method has higher accuracy.
In addition, according to the abnormality detection method, the controller and the wind turbine, which are disclosed by the embodiment of the application, the angle deviation of the blade can be detected, the positive and negative changes of the angle deviation of the blade can be detected, meanwhile, the early abnormality of the device of the pitch system in the running process of the wind turbine can be identified, the alarm is timely given, and the stable and safe running of the wind turbine is ensured.
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The foregoing and other objects and features of embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings in which the embodiments are shown, in which:
FIG. 1 is a graph illustrating blade pitch angle during a pitch operation;
FIG. 2 is a graph illustrating a pitch angle deviation between pitch angle curve 101 and another pitch angle curve;
fig. 3 is a diagram illustrating an example of a disadvantage of a conventional method of detecting a blade angle deviation;
fig. 4A and 4B are graphs showing the angular deviation and a spectrogram of the angular deviation, respectively, in the case of normal pitching;
FIG. 5 is a spectrum graph showing angular deviation in the presence of significant pitch angle deviation as shown in FIG. 2;
FIG. 6 is a flowchart illustrating a method of anomaly detection for a wind turbine pitch system according to an embodiment of the present disclosure;
fig. 7 is a block diagram illustrating a controller according to an embodiment of the present disclosure.
Detailed Description
The following detailed description is provided to assist the reader in obtaining a thorough understanding of the methods, apparatus, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and/or systems described herein will be apparent after an understanding of the present disclosure. For example, the order of operations described herein is merely an example and is not limited to those set forth herein, but may be altered as will be apparent after an understanding of the disclosure of the application, except for operations that must occur in a specific order. Furthermore, descriptions of features known in the art may be omitted for clarity and conciseness.
The following first describes the deficiencies of the existing blade angle deviation detection method, the operation condition of the pitch system of the wind turbine, and the working principle of the abnormality detection method of the pitch system of the wind turbine according to the embodiments of the present disclosure.
The existing method for detecting the angle deviation of the blade mainly comprises the following two methods.
One method is to alarm after detecting that the difference in blade angle is greater than a certain value for a certain time. The disadvantage of this method is that: firstly, the alternating change of the blade angle cannot be detected, and secondly, the timing parameter of the timer needs to be set. However, in the running process of the pitch system, the three blades are not always in angle deviation, but are sometimes in larger angle deviation and sometimes in smaller angle deviation, and if the conditions change frequently, the timer is easy to be caused to be unable to reach the timing time length all the time. In addition, this method generally needs to detect the magnitude of the angle deviation, and the magnitude of the angle deviation is random in the operation process of the pitch system, and even there may be a situation that the magnitude of the angle deviation is not large, but the change frequency is high.
Another method detects that the number of angular deviations greater than the threshold exceeds a certain threshold. The disadvantage of this method is that: firstly, a detection threshold value needs to be set, secondly, the change condition of each angle deviation cannot be identified, and the detection result can be the result of accumulation of multiple intermittent angle deviations, so that the detection accuracy is lower.
FIG. 1 is a graph illustrating blade pitch angle during a pitch operation. In fig. 1, the abscissa represents the time value, the ordinate represents the blade pitch angle value, and curves 101, 102, 103 represent three blades, respectively. As shown in fig. 1, significant deviations between blade pitch angles occur mainly because early wear of the electromagnetic brake valve inside the pitch motor corresponding to curve 101 occurs, resulting in a slight stall of the pitch motor.
More specifically, as shown in fig. 1, there is an angular deviation between the curve 101 and the curves 102, 103, and the angular deviation shows a tendency to gradually increase. From time-50, the angular deviation between curve 101 and curves 102, 103 begins to increase and exhibits an alternating condition, such as between-25 and-20, the value of curve 101 is greater, between-18 and-12, the value of curve 101 is smaller, and between-20 and-18, the three-blade angle is substantially close. For this alternation, it is difficult to perform accurate detection by the existing method.
FIG. 2 is a graph illustrating a pitch angle deviation between a pitch angle curve 101 and another pitch angle curve (e.g., curve 102 or curve 103).
Referring to fig. 2, the abscissa represents a sampling point, and the ordinate represents the magnitude of an angular deviation (i.e., a difference in pitch angles). As shown in fig. 2, the angular deviation exhibits sinusoidal fluctuations, and the amplitude of the fluctuations is not a constant value. In actual operation of the wind turbine, the fluctuation period of the blade angle deviation is changed along with the change of wind conditions and impeller rotating speed, and the fluctuation frequency is also changed along with the change of wind speed. That is, the magnitude, frequency and deviation time window of the blade angle deviation are all changed along with the change of the running condition of the wind turbine generator. Therefore, it is difficult to set a proper detection parameter or threshold value by the conventional method of detecting angular deviation.
Fig. 3 is a diagram illustrating an example of a disadvantage of a conventional method of detecting a blade angle deviation.
Referring to fig. 2, the abscissa represents a sampling point, the ordinate represents the magnitude of the angular deviation, t represents the timing duration of the timer, i.e., the period of time between the two vertical dashed lines, and the two horizontal dashed lines represent the threshold value of the blade angular deviation. In the time range t, the value of the greater blade angle deviation lies within the range of the two horizontal dash-dot lines. This phenomenon results in the following: at about 2620, the blade angle deviation is greater than the threshold, the timer starts, and begins to count, but by 2840, the blade angle deviation is less than the threshold again, at which point the timer will be disconnected and reset due to the triggering condition not being met. Repeating the steps, the timer finally cannot reach the timing duration, and therefore the blade angle deviation alarm cannot be triggered.
Although the threshold and the time length of the timer can be modified, the fluctuation period of the blade angle deviation is changed along with the change of wind conditions and the rotation speed of the impeller, and the fluctuation frequency is also changed along with the change of wind, so that for long-term and batch wind turbines, it is difficult to set general detection parameters (namely, the threshold and the time length of the timer) to detect the blade angle deviation. That is, if the detection parameters are set according to the data of the primary failure file, erroneous detection or missing detection of the blade angle breakage deviation is extremely likely to occur in the rest of the operation period or in other wind turbines.
In view of the drawbacks of the existing methods for detecting blade angle deviations, the abnormality detection method of a wind turbine pitch system according to embodiments of the present disclosure mainly includes obtaining a difference between pitch angles of every two blades, calculating a numerical statistic reflecting a distribution characteristic of the difference between pitch angles of every two blades, and determining whether a deviation occurs in three-blade angles based on the calculated numerical statistic. In addition, the abnormality detection method of the wind turbine generator pitch system according to the embodiment of the disclosure further includes performing frequency domain transformation on the obtained difference of pitch angles of every two blades, and performing spectrum analysis on a spectrum obtained through the frequency domain transformation.
According to embodiments of the present disclosure, the numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of each two blades may include a kurtosis value, but is not limited thereto. The kurtosis value represents the degree of smoothness of the curve. When the kurtosis value is less than 3, the curve will be relatively "flat" and when the kurtosis value is greater than 3, the curve will be relatively "steep". In particular, when the kurtosis value is negative, the representation curve is very gentle.
The kurtosis value K may be calculated by equation (1).
Wherein N represents the number of samples, X i Represents the sample value, μ represents the average value of the samples, σ represents the standard deviation of the samples. The standard deviation σ can be calculated by equation (2).
Taking the example of the apparent angular deviation shown in fig. 2, the kurtosis value K of the angular deviation reaches 4.416. However, in the case of normal pitch, the kurtosis value K of the angle deviation is only-0.17.
Fig. 4A and 4B are graphs showing angular deviation and a spectrum chart showing angular deviation, respectively, in the case of normal pitching.
Referring to fig. 4A, the abscissa represents time and the ordinate represents the magnitude of the angular deviation. As shown in fig. 4A, the value of the angular deviation fluctuates between-0.2 and 0.2. Referring to fig. 4B, the abscissa represents frequency and the ordinate represents signal strength. As shown in fig. 4B, the maximum signal strength of the spectrum of the angular deviation is only 0.030.
FIG. 5 is a spectrum graph showing angular deviations in the presence of significant pitch angle deviations as shown in FIG. 2.
Referring to fig. 5, the abscissa represents frequency and the ordinate represents signal strength. As shown in fig. 5, the maximum signal strength of the spectrum of the angular deviation reaches 0.348, which corresponds to a frequency value of about 0.1Hz.
Fig. 6 is a flowchart illustrating an anomaly detection method for a wind turbine pitch system according to an embodiment of the present disclosure. The anomaly detection method may be performed by a main controller, a pitch controller, and/or other dedicated controllers of the wind turbine.
Referring to fig. 6, in step S601, pitch angles of three blades at respective sampling moments are acquired. For example, the pitch angles of three blades at each sampling instant may be obtained during a pitch operation of the wind turbine.
In step S602, the pitch angles of three blades at the same sampling time are different from each other, and the difference between the pitch angles of every two blades is obtained. For example, the pitch angle of the first blade and the second blade at the same sampling time may be differed to obtain the difference between the pitch angles of the first blade and the second blade, the pitch angle of the first blade and the third blade at the same sampling time may be differed to obtain the difference between the pitch angles of the first blade and the third blade, and the pitch angle of the second blade and the third blade at the same sampling time may be differed to obtain the difference between the pitch angles of the second blade and the third blade. According to the embodiment of the disclosure, when pitch angles of three blades at the same sampling time are different from each other, an absolute value of the difference between the pitch angles of every two blades does not need to be obtained. In this way, the change rule of the blade angle deviation can be determined according to the change trend of the positive and negative values of the difference of the pitch angles of every two blades.
In step S603, a numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of each of the two blades is calculated based on the obtained difference between the pitch angles of each of the two blades. In other words, the numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of the first blade and the second blade may be calculated based on the obtained difference between the pitch angles of the first blade and the second blade, the numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of the first blade and the third blade may be calculated based on the obtained difference between the pitch angles of the first blade and the third blade, and the numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of the second blade and the third blade may be calculated based on the obtained difference between the pitch angles of the second blade and the third blade.
More specifically, when the number of acquired differences in pitch angles of every two blades reaches a preset number threshold (e.g., without limitation, 50, 100, etc.), a numerical statistic reflecting the distribution characteristics of the differences in pitch angles of every two blades may be calculated based on the acquired differences in pitch angles of every two blades. Alternatively, a numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of each two blades may be calculated based on the obtained difference between the pitch angles of each two blades when the duration of obtaining the difference between the pitch angles of each two blades reaches a preset time threshold (for example, but not limited to, 1s, 2s, etc.). In other words, when the number of obtained differences in pitch angles of the first blade and the second blade, the first blade and the third blade, and the second blade and the third blade reaches a preset number threshold, or when the number of obtained differences in pitch angles of the first blade and the second blade, the first blade and the third blade, and the second blade and the third blade reach a preset time threshold, numerical statistics reflecting distribution characteristics of the differences in pitch angles of the first blade and the second blade, numerical statistics reflecting distribution characteristics of the differences in pitch angles of the first blade and the third blade, and numerical statistics reflecting distribution characteristics of the differences in pitch angles of the second blade and the third blade may be calculated, respectively. As described above, the numerical statistic may include, but is not limited to, a kurtosis value. The kurtosis value may be calculated based on the number of acquired pitch angles per two blades, the pitch angle per two blades at each sampling time, the average value and standard deviation of the pitch angle per two blades. That is, the kurtosis value of the difference between the pitch angles of the first blade and the second blade, the kurtosis value of the difference between the pitch angles of the first blade and the third blade, and the kurtosis value of the difference between the pitch angles of the second blade and the third blade may be calculated based on the above equations (1) and (2).
In step S604, it is determined whether an abnormality occurs in the wind turbine pitch system based on the calculated numerical statistic. Here, an abnormality in the pitch system of the wind turbine generator may indicate a deviation in the angle of the three blades. For example, when the calculated kurtosis value is greater than a first kurtosis threshold (e.g., without limitation, 1.0), an anomaly may be determined for the wind turbine pitch system. More specifically, an abnormality in the wind turbine pitch system may be determined when a kurtosis value of a difference in pitch angles of the first blade and the second blade, a kurtosis value of a difference in pitch angles of the first blade and the third blade, and/or a kurtosis value of a difference in pitch angles of the second blade and the third blade is greater than a first kurtosis threshold. In other words, when at least one of the kurtosis value of the difference between the pitch angles of the first blade and the second blade, the kurtosis value of the difference between the pitch angles of the first blade and the third blade, and the kurtosis value of the difference between the pitch angles of the second blade and the third blade is greater than the first kurtosis threshold, it may be determined that an abnormality occurs in the wind turbine pitch system. Here, since whether an abnormality occurs in the wind turbine generator system pitch system is determined by a kurtosis value based on a difference in pitch angles instead of a value of a difference in pitch angles, it is unnecessary to set a blade angle deviation detection threshold and a detection window, and the detection is insensitive to instantaneous changes in blade angle deviation, and false detection due to angle jumps is not caused.
Optionally, the abnormality detection method may further include the steps of: and carrying out frequency domain transformation on the obtained difference of the pitch angles of every two blades to obtain a frequency spectrum of the difference of the pitch angles of every two blades. That is, the frequency domain transformation may be performed on the difference in pitch angle between the first blade and the second blade, the difference in pitch angle between the first blade and the third blade, and the difference in pitch angle between the second blade and the third blade, respectively, to obtain a spectrum of the difference in pitch angle between the first blade and the second blade, a spectrum of the difference in pitch angle between the first blade and the third blade, and a spectrum of the difference in pitch angle between the second blade and the third blade. Thus, in step S604, when the maximum value of the signal intensity of the frequency spectrum and the frequency corresponding to the maximum value of the signal intensity of the frequency spectrum satisfy the first preset condition, and the kurtosis value satisfies the second preset condition, it may be determined that an abnormality occurs in the pitch system of the wind turbine. Here, the first preset condition includes that a maximum value of signal intensity of the frequency spectrum is greater than a preset intensity threshold (for example, but not limited to, 0.2) and a frequency corresponding to the maximum value of signal intensity is not zero, and the second preset condition includes that a kurtosis value is greater than the first kurtosis threshold. Here, the maximum value of the signal intensity may represent the blade angle deviation duration. For example, the greater the maximum value of signal strength, the longer the blade angle deviation duration.
More specifically, when the maximum value of the signal intensity of the frequency spectrum of the difference between the pitch angles of the first blade and the second blade is greater than a preset intensity threshold value, the frequency corresponding to the maximum value of the signal intensity of the frequency spectrum is not zero, and the kurtosis value of the difference between the pitch angles of the first blade and the second blade is greater than a first kurtosis threshold value, it may be determined that the wind turbine pitch system is abnormal; and/or when the maximum value of the signal intensity of the frequency spectrum of the difference between the pitch angles of the first blade and the third blade is larger than a preset intensity threshold value, the frequency corresponding to the maximum value of the signal intensity of the frequency spectrum is not zero, and the kurtosis value of the difference between the pitch angles of the first blade and the third blade is larger than a first kurtosis threshold value, the abnormal occurrence of the pitch system of the wind turbine generator can be determined; and/or when the maximum value of the signal intensity of the frequency spectrum of the difference between the pitch angles of the second blade and the third blade is larger than a preset intensity threshold value, the frequency corresponding to the maximum value of the signal intensity of the frequency spectrum is not zero, and the kurtosis value of the difference between the pitch angles of the second blade and the third blade is larger than a first kurtosis threshold value, the abnormal occurrence of the pitch system of the wind turbine can be determined. In other words, it may be determined that an anomaly has occurred in the pitch system of the wind turbine when at least one of the following three conditions exists: (1) The maximum value of the signal intensity of the frequency spectrum of the difference between the pitch angles of the first blade and the second blade is larger than a preset intensity threshold value, the frequency corresponding to the maximum value of the signal intensity of the frequency spectrum is not zero, and the kurtosis value of the difference between the pitch angles of the first blade and the second blade is larger than a first kurtosis threshold value; (2) The maximum value of the signal intensity of the frequency spectrum of the difference between the pitch angles of the first blade and the third blade is larger than a preset intensity threshold value, the frequency corresponding to the maximum value of the signal intensity of the frequency spectrum is not zero, and the kurtosis value of the difference between the pitch angles of the first blade and the third blade is larger than a first kurtosis threshold value; (3) The maximum value of the signal intensity of the frequency spectrum of the difference between the pitch angles of the second blade and the third blade is larger than a preset intensity threshold value, the frequency corresponding to the maximum value of the signal intensity of the frequency spectrum is not zero, and the kurtosis value of the difference between the pitch angles of the second blade and the third blade is larger than a first kurtosis threshold value.
Optionally, the abnormality detection method may further include the steps of: and when the kurtosis value is larger than the second kurtosis threshold value, triggering the wind turbine to stop in a fault mode. Here, the second kurtosis threshold is greater than the first kurtosis threshold. For example, the second kurtosis threshold may be, but is not limited to, 10. For example, a wind turbine generator system may be triggered to fail shutdown when a kurtosis value of a difference in pitch angles of the first blade and the second blade, a kurtosis value of a difference in pitch angles of the first blade and the third blade, and/or a kurtosis value of a difference in pitch angles of the second blade and the third blade is greater than a second kurtosis threshold.
Fig. 7 is a block diagram illustrating a controller according to an embodiment of the present disclosure. The controller can be a main controller of the wind turbine generator and can also be any special controller in the wind turbine generator.
Referring to fig. 7, a controller 700 according to an embodiment of the present disclosure may include a processor 710 and a memory 720. Processor 710 may include, but is not limited to, a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a microcomputer, a Field Programmable Gate Array (FPGA), a system on a chip (SoC), a microprocessor, an Application Specific Integrated Circuit (ASIC), and the like. Memory 720 may store computer programs to be executed by processor 710. Memory 720 may include high-speed random access memory and/or a non-volatile computer-readable storage medium. When processor 710 executes the computer program stored in memory 720, an anomaly detection method for a wind turbine pitch system as described above may be implemented.
Alternatively, the controller 700 may communicate with other various components in the wind farm in a wired or wireless communication manner, as well as with other devices in the wind farm in a wired or wireless communication manner. In addition, the controller 700 may communicate with devices external to the wind farm in a wired or wireless communication.
According to an embodiment of the present disclosure, a wind turbine may be provided that includes a controller 700.
The abnormality detection method of the wind turbine pitch system according to the embodiments of the present disclosure may be written as a computer program and stored on a computer-readable storage medium. When the computer program is executed by the processor, the abnormality detection method of the wind turbine generator pitch system can be realized. Examples of the computer readable storage medium include: read-only memory (ROM), random-access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), flash memory, nonvolatile memory, CD-ROM, CD-R, CD + R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD + R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, blu-ray or optical disk storage, hard Disk Drives (HDD), solid State Disks (SSD), card memory (such as multimedia cards, secure Digital (SD) cards or ultra-fast digital (XD) cards), magnetic tape, floppy disks, magneto-optical data storage, hard disks, solid state disks, and any other means configured to store computer programs and any associated data, data files and data structures in a non-transitory manner and to provide the computer programs and any associated data, data files and data structures to a processor or computer to enable the processor or computer to execute the programs. In one example, the computer program and any associated data, data files, and data structures are distributed across networked computer systems such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner by one or more processors or computers.
On the other hand, the abnormality detection method of a wind turbine pitch system according to embodiments of the present disclosure may be implemented as a computer program product comprising a computer program which, when executed by a processor, implements the abnormality detection method of a wind turbine pitch system as described above.
According to the abnormality detection method, the controller and the wind turbine, which are disclosed by the embodiment of the application, the wind turbine variable pitch system does not need to be provided with a blade angle deviation detection threshold value and a detection window, and the detection result can embody the change rule of the blade angle deviation and the duration time of the change rule, so that the abnormality detection method, the controller and the wind turbine can be directly suitable for detection of different working conditions, different wind conditions and different wind turbines. Meanwhile, the detection result is insensitive to the instantaneous change of the angle deviation of the blade, and false detection cannot be caused by angle jump, so that the method has higher accuracy.
In addition, according to the abnormality detection method, the controller and the wind turbine, which are disclosed by the embodiment of the application, the angle deviation of the blade can be detected, the positive and negative changes of the angle deviation of the blade can be detected, meanwhile, the early abnormality of the device of the pitch system in the running process of the wind turbine can be identified, the alarm is timely given, and the stable and safe running of the wind turbine is ensured.
Although a few 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 principles and spirit of the disclosure, the scope of which is defined in the claims and their equivalents.
Claims (12)
1. An anomaly detection method for a pitch system of a wind turbine generator, the anomaly detection method comprising:
obtaining pitch angles of three blades at each sampling moment;
the pitch angles of three blades at the same sampling moment are subjected to two-by-two difference, and the difference of the pitch angles of every two blades is obtained;
calculating a numerical statistic reflecting the distribution characteristic of the difference between the pitch angles of every two blades based on the obtained difference between the pitch angles of every two blades;
and determining whether the variable pitch system of the wind turbine generator is abnormal or not based on the numerical statistic.
2. The abnormality detection method according to claim 1, characterized in that the abnormality detection method further comprises:
calculating a numerical statistic reflecting a distribution characteristic of the difference between the pitch angles of every two blades based on the obtained difference between the pitch angles of every two blades in response to the obtained number of the difference between the pitch angles of every two blades reaching a preset number threshold; or,
in response to obtaining a difference in pitch angle of each two blades reaching a preset time threshold, calculating a numerical statistic reflecting a distribution characteristic of the difference in pitch angle of each two blades based on the obtained difference in pitch angle of each two blades.
3. The anomaly detection method of claim 1, wherein the numerical statistic includes a kurtosis value.
4. The anomaly detection method of claim 3, wherein determining whether an anomaly has occurred in a pitch system of a wind turbine based on the numerical statistic comprises:
and determining that the pitch system of the wind turbine generator is abnormal in response to the kurtosis value being greater than a first kurtosis threshold.
5. The abnormality detection method according to claim 3, wherein the step of calculating a numerical statistic reflecting a distribution characteristic of a difference between pitch angles of each two blades based on the obtained difference between pitch angles of each two blades includes:
and calculating the kurtosis value based on the acquired number of the pitch angles of every two blades, the pitch angle difference of every two blades at each sampling time, the average value and standard deviation of the pitch angle difference of every two blades.
6. The abnormality detection method according to claim 3, characterized in that the abnormality detection method further comprises:
and carrying out frequency domain transformation on the obtained difference of the pitch angles of every two blades to obtain a frequency spectrum of the difference of the pitch angles of every two blades.
7. The anomaly detection method of claim 5, wherein determining whether an anomaly has occurred in a pitch system of a wind turbine based on the numerical statistic comprises:
and responding to the maximum value of the signal intensity of the frequency spectrum and the frequency corresponding to the maximum value of the signal intensity to meet a first preset condition, and responding to the kurtosis value to meet a second preset condition, and determining that the pitch system of the wind turbine generator is abnormal.
8. The abnormality detection method according to claim 7, characterized in that,
the first preset condition includes: the maximum value of the signal intensity of the frequency spectrum is larger than a preset intensity threshold value, and the frequency corresponding to the maximum value of the signal intensity is not zero;
the second preset condition includes: the kurtosis value is greater than a first kurtosis threshold.
9. The abnormality detection method according to claim 4 or 8, characterized in that the abnormality detection method further comprises:
and triggering the wind turbine generator to stop in a fault mode in response to the kurtosis value being greater than a second kurtosis threshold, wherein the second kurtosis threshold is greater than the first kurtosis threshold.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the abnormality detection method according to any one of claims 1 to 9.
11. A controller, the controller comprising:
a processor; and
a memory storing a computer program which, when executed by a processor, implements the anomaly detection method of any one of claims 1 to 9.
12. A wind turbine comprising the controller of claim 11.
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