CN115523103A

CN115523103A - Fluctuation detection method and fluctuation detection device for operating data of wind generating set

Info

Publication number: CN115523103A
Application number: CN202110702820.5A
Authority: CN
Inventors: 马磊; 霍钧; 赵静菊
Original assignee: Xinjiang Goldwind Science and Technology Co Ltd
Current assignee: Xinjiang Goldwind Science and Technology Co Ltd
Priority date: 2021-06-24
Filing date: 2021-06-24
Publication date: 2022-12-27

Abstract

The utility model discloses a fluctuation detection method and a fluctuation detection device of wind generating set operating data, the fluctuation detection method comprises: continuously sampling the operating data of the wind generating set; calculating an average value of the running data of the wind generating set in a preset detection period; determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the wind generating set operation data in the preset detection period and the average value; and responding to the fact that the wind generating set operation data in the preset detection period are distributed at equal intervals, and outputting information indicating that the wind generating set operation data in the preset detection period are subjected to sine fluctuation.

Description

Fluctuation detection method and fluctuation detection device for operating data of wind generating set

Technical Field

The present disclosure relates generally to the field of wind power generation technology, and more particularly, to a method and an apparatus for detecting fluctuation of operating data of a wind turbine generator system.

Background

With the large-scale capacity of the wind generating set, advanced wind power technologies such as variable pitch control, variable speed constant frequency and the like have become the mainstream control modes of the current wind generating set. The generator is an important device for converting wind energy into electric energy in the wind generating set, and not only directly influences the quality and efficiency of output electric energy, but also influences the performance and structural complexity of the whole wind generating set.

A wind generating set is a complex system. At present, the MW-level permanent magnet wind driven generator highly integrates comprehensive subjects such as aerodynamics, structural mechanics, electromechanics, material science, power electronic technology, power system analysis, relay protection technology, automatic control technology, modern communication and the like, and becomes a set of complex energy conversion system, so that the occurrence of the same fault can be caused by different reasons. Taking the "three-axis angle inconsistency" fault of the pitch system as an example, the cause of the fault may be a fault of an encoder of the pitch system, a fault of a power supply of the encoder, a blade jamming of the pitch system, or a fault of a pitch drive.

At present, the fault detection of the wind generating set usually adopts a simple numerical comparison, for example, when the rotating speed is greater than a certain value, or the voltage of a backup power supply is lower than a certain value, the fault is triggered. However, in addition to the increase or decrease in the values, frequent data fluctuations occur in many cases when components of the wind energy installation fail and the wind energy installation returns to normal after being shut down. As a result, on one hand, after the fault is reported, the operation and maintenance personnel are not easy to find the cause of the fault. On the other hand, the timer in the controller is characterized in that after the condition is switched on, the timer starts to time, after the condition is switched off, the timer stops working and resets, and frequent fluctuation of data can also cause the timer to be frequently started and reset, so that the timer cannot reach the timing time, so that the fault cannot be normally triggered, the troubleshooting is difficult, and even if the fault cannot be normally triggered, the safety of the unit can be damaged.

In order to analyze the fault reason of the wind generating set, the operation data of the wind generating set needs to be analyzed and diagnosed, the analysis and diagnosis can automatically, accurately and timely record the change conditions of various electrical quantities in the processes before and after the fault at the fault occurrence moment, and the analysis and comparison of the electrical quantities play an important role in analyzing and processing the accident, judging whether the protection acts correctly and safely and reliably operating the wind generating set. In the analysis and diagnosis, whether the operation data has periodic and sinusoidal fluctuation is judged, and the method is a key factor for monitoring whether the operation of the wind generating set is stable and normal. For example, if the pitch variation speed fluctuates sinusoidally and frequently, the pitch variation motor is too frequent in pitch adjustment and reversing, and the temperature of the pitch variation motor is increased too fast; if the rotating speed of the generator fluctuates sinusoidally and frequently, the control of the generator is unstable, and the generator is out of order, which can affect the load and vibration of the wind generating set.

Disclosure of Invention

The embodiment of the disclosure provides a fluctuation detection method and a fluctuation detection device for wind generating set operation data, which can be directly applied to detection of various operation data, do not need to set parameters such as detection period and detection amplitude, and have wide applicability.

In one general aspect, there is provided a fluctuation detection method of wind turbine generator system operation data, the fluctuation detection method comprising: continuously sampling the operating data of the wind generating set; calculating an average value of the running data of the wind generating set in a preset detection period; determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the wind generating set operation data in the preset detection period and the average value; and responding to the fact that the wind generating set operation data in the preset detection period are distributed at equal intervals, and outputting information indicating that the wind generating set operation data in the preset detection period are subjected to sine fluctuation.

In another general aspect, there is provided a wind turbine generator system operation data fluctuation detection apparatus, including: a sampling unit configured to: continuously sampling the operating data of the wind generating set; a computing unit configured to: calculating an average value of the running data of the wind generating set in a preset detection period; a determination unit configured to: determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the wind generating set operation data in the preset detection period and the average value; an output unit configured to: and responding to the fact that the wind generating set operation data in the preset detection period are distributed at equal intervals, and outputting information indicating that the wind generating set operation data in the preset detection period are subjected to sine fluctuation.

In another general aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the method for fluctuation detection of wind park operational data 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 method of fluctuation detection of wind turbine generator set operating data as described above.

The fluctuation detection method and the fluctuation detection device for the wind generating set operation data in the embodiment of the disclosure can not only solve the problem that real data fluctuation cannot be detected due to small statistical value caused by long data fluctuation period, but also automatically filter short-time and accidental data jump and interference, and ensure the reliability of fluctuation detection. On the other hand, according to the fluctuation detection method and the fluctuation detection device for the wind generating set operation data in the embodiment of the disclosure, the detection accuracy is not affected by the detection period and the data fluctuation amplitude, so that the method and the device can be directly applied to the fluctuation detection of various types of data.

In addition, the fluctuation detection method and the fluctuation detection device for the wind generating set operation data in the embodiment of the disclosure have no requirement on the detection threshold setting of the operation data, so that the parameter adjustment for the wind generating set is not required to be frequently performed. Meanwhile, the fluctuation detection method is simple and convenient to calculate and high in efficiency, can be directly realized in a PLC (programmable logic controller), and can ensure the detection accuracy.

Additional aspects and/or advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.

Drawings

The above and other objects and features of the embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings illustrating the embodiments, in which:

fig. 1 is a graph showing a generator rotation speed as an object of fluctuation detection;

FIG. 2 is a graph showing pitch given speed and pitch actual speed as objects of surge detection;

fig. 3 is a diagram showing an example of a conventional data fluctuation detection method;

FIG. 4 is a diagram illustrating the concept of a method of fluctuation detection of wind park operational data according to an embodiment of the present disclosure;

FIG. 5 is a flow chart illustrating a method of fluctuation detection of wind park operational data according to an embodiment of the present disclosure;

FIG. 6 is a block diagram illustrating a wind park operational data fluctuation detection apparatus according to an exemplary embodiment of the present disclosure;

fig. 7 is a block diagram illustrating a controller in an embodiment in accordance with the present disclosure.

Detailed Description

The following detailed description is provided to assist the reader in obtaining a thorough understanding of the methods, devices, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and/or systems described herein will be apparent to those skilled in the art after reviewing the disclosure of the present application. For example, the order of operations described herein is merely an example, and is not limited to those set forth herein, but may be changed as will become apparent after understanding the disclosure of the present application, except to the extent that operations must occur in a particular order. Moreover, descriptions of features known in the art may be omitted for greater clarity and conciseness.

At present, there are several methods for detecting the occurrence of periodic fluctuation of data.

One method is to consider that the data fluctuates when the data value is judged to be larger than a certain value once or many times. However, this method cannot judge the timing of data transitions. For example, data fluctuation means that data repeatedly jumps in a short time, and only the jumps are recorded, which is likely to cause misjudgment due to short-time interference. In addition, the threshold value is not easily determined for the detected data value size. For example, the voltage of the super capacitor is 85V normally, and the voltage value greater than 91V is abnormal, but the maximum value of the data jump may be 90V, which may cause missing detection. More importantly, when the data fluctuates sinusoidally periodically, the data does not have to jump, and in this case, the method cannot detect the periodic sinusoidal fluctuation of the data.

Alternatively, the data change slope is judged to determine whether the data fluctuates. This method can detect whether the data is rising normally or jumping in fluctuation to some extent, but it cannot accurately reflect the fluctuation amplitude of the data, especially the fluctuation period of the data. For example, the slope of the data from 85V to 88V is almost equal to the slope of the data from 85V to 91V. On the other hand, when the fluctuation cycle of data is long, the difference in detection time causes a calculation deviation. If the detection period is too short, the slope of single change is detected, and the overall change condition of the data cannot be reflected; if the detection period is too long, the peaks may be skipped, resulting in detection errors.

Another method is a variance method, the detection result of which depends on the detection period, and on the other hand, this method can only detect whether the data deviates from the normal value, and cannot detect the fluctuation tendency of the data. Furthermore, the variance cannot filter false detections caused by accidental jumps.

Fig. 1 is a graph showing the generator rotation speed as an object of the fluctuation detection. In fig. 1, the abscissa represents the time value and the ordinate represents the rotation speed value. As can be seen from fig. 1, the generator speed (also the impeller speed) fluctuates in a sinusoidal manner, which is an abnormal fluctuation phenomenon of the operation data of the wind turbine generator system. In addition, during the periodic fluctuation of the generator rotation speed, the generator rotation speed generally has a gradually descending trend. Therefore, if only the maximum value and the minimum value of the rotation speed value are detected, since the generator rotation speed varies with the variation of the wind speed, the reference value thereof is not fixed, and it is difficult to effectively detect the fluctuation with the threshold method.

Fig. 2 is a graph showing the pitch given speed and the pitch actual speed that are the objects of the fluctuation detection. Fig. 3 is a diagram illustrating an example of a conventional data fluctuation detection method.

In fig. 2 and 3, the abscissa represents the time value and the ordinate represents the velocity value. It can be seen from fig. 2 that both the pitch given speed 201 and the pitch actual speed 202 fluctuate in a sinusoidal-like fashion. Referring to fig. 3 in combination, if data fluctuation detection is performed using a data change slope, only a change slope within a certain range can be detected, and the detection accuracy depends on the fluctuation period of data. However, the fluctuation period of the data is unknown in advance. As shown in fig. 3, in the interval from t1 to t2, the data variation is small, so if the detection period is short, the detected curve variation slope is small, and the data fluctuation detection cannot be realized; on the other hand, if the detection period is too long, for example, in the interval t2 to t3 shown in fig. 3, the ordinate values at two times are equivalent, and the change slope of the detected curve is still small, which means that no data fluctuation occurs in the interval t2 to t3, but actually, the data fluctuation in the interval becomes large. Therefore, the data fluctuation trend cannot be accurately detected by using the data change slope for data fluctuation detection.

On the other hand, if the variance value is used for data fluctuation detection, the detection accuracy also depends on the detection period, which is too short, the calculated variance value will be small, and the detection period will be too large, which in turn depends on the magnitude of the data fluctuation. Furthermore, if a short time transition occurs in the data (e.g., at time t 4), the calculated variance value may also be large. Therefore, the data fluctuation detection using the variance values can only detect the degree of deviation of the data from the normal values, and cannot detect the trend of the data fluctuation.

Fig. 4 is a diagram illustrating a concept of a fluctuation detection method of wind turbine generator system operation data according to an embodiment of the present disclosure.

Referring to fig. 4, in the fluctuation detection method for the operating data of the wind turbine generator system in the embodiment of the present disclosure, an average value of the operating data is obtained according to a certain detection period, quantization processing is performed according to whether the operating data at each sampling time is greater than the average value or less than or equal to the average value, and then a distribution curve of the operating data and the number of corresponding continuous operating data are determined; if the distribution curve of the operation data shows periodic variation and the number of the corresponding continuous operation data is large, the operation data is considered to have sinusoidal fluctuation. As shown in fig. 4, the distribution curve between the high and low phases is generated by comparing the operation data at each sampling time with the average value. The average value can be changed along with the change of the operation data without being influenced by the detection period, so that compared with the existing fluctuation detection method, the fluctuation detection method based on the data distribution curve is more reliable and accurate.

Fig. 5 shows a flow chart of a method of fluctuation detection of wind park operational data according to an embodiment of the present disclosure. The fluctuation detection method of the wind generating set operation data in the embodiment of the disclosure can be realized in a main controller of the wind generating set, and can also be realized in any special controller in the wind generating set.

Referring to fig. 5, in step S501, wind turbine generator set operating data may be continuously sampled. Here, the wind park operational data may be, for example, generator speed, pitch speed, generator torque, etc. The wind turbine generator system operation data may be obtained by various detection devices, which are not limited in this disclosure.

In step S502, an average value of the wind turbine generator system operation data within a preset detection period may be calculated. Here, the preset detection period may include a plurality of sampling intervals at which the wind turbine generator system operation data is sampled. For example, the sampling interval may be 20ms, and the preset detection period may be 500ms. However, the above values are merely examples, and the sampling interval and the duration of the preset detection period may be appropriately adjusted as needed.

Next, in step S503, it may be determined whether the wind generating set operation data in the preset detection period has an equal interval distribution phenomenon based on the wind generating set operation data in the preset detection period and the calculated average value.

Specifically, the wind turbine generator system operation data at each sampling time within the preset detection period may be compared with the calculated average value, and whether the wind turbine generator system operation data within the preset detection period is equally spaced may be determined based on the comparison result. When the wind generating set operation data at each sampling moment in the preset detection period is compared with the calculated average value, if the wind generating set operation data at the sampling moment is larger than the calculated average value, a value 1 is stored in a memory, and if the wind generating set operation data at the sampling moment is not larger than the calculated average value, a value 0 is stored in the memory. The memory may be a memory in the controller running the fluctuation detection method, or may be another memory provided in the wind turbine generator set. According to the embodiment of the disclosure, the significance of comparing the wind generating set operation data at each sampling moment in the preset detection period with the calculated average value is as follows: the average value can be automatically adjusted along with the rising and falling trend of the running data, so that the detection accuracy is ensured.

After comparing the wind generating set operation data at each sampling moment within the preset detection period with the calculated average value, a counter may be set to count. Thereafter, whether the equal interval distribution phenomenon occurs in the operation data of the wind generating set in the preset detection period can be determined based on the counting value of the counter. Here, the counter may be a memory in the controller that operates the fluctuation detection method, or may be a dedicated counter provided in the wind turbine generator set. The set counter may count according to the following rules: if the number of consecutively stored values 1 is greater than the first threshold value and then the number of consecutively stored values 0 is greater than the first threshold value, increasing the count value of the counter by 1; if the number of consecutively stored values 0 is greater than the first threshold value and then the number of consecutively stored values 1 is greater than the first predetermined threshold value, the count value of the counter is incremented by 1. Alternatively, if the count value of the counter is greater than the second threshold value, it may be determined that the wind generating set operation data is distributed at equal intervals in the preset detection period. In an embodiment of the present disclosure, the first threshold may be an integer, for example, not less than 5, and the second threshold may be an integer, for example, not less than 2.

In addition, the counter can count according to the following rules: clearing the count value of the counter if the number of the continuously stored values 1 is greater than a first threshold value and then the number of the continuously stored values 0 is not greater than the first threshold value; if the number of consecutively stored values 0 is greater than the first threshold value and then the number of consecutively stored values 1 is not greater than the first predetermined threshold value, the count value of the counter is cleared. Alternatively, it may be determined according to the above rule that, in the case where the counter is cleared, if the number of

values

1 or 0 that are continuously stored is not greater than the first threshold value, the count value of the counter is not increased.

If it is determined that the wind generating set operation data in the preset detection period has the equal interval distribution phenomenon, in step S504, information indicating that the wind generating set operation data in the preset detection period has sine fluctuation may be output. According to the embodiment of the disclosure, the sign indicating that the wind generating set operation data in the preset detection period has sine fluctuation and the maximum value and the minimum value of the wind generating set operation data in the preset detection period can be output. Here, one specific implementation way of outputting the flag indicating that the sinusoidal fluctuation occurs in the wind turbine generator system operation data within the preset detection period is to output an alarm to prompt an operation and maintenance person to analyze the operation data and pay attention to the operation condition of the wind turbine generator system. In addition, the maximum value and the minimum value of the operation data of the wind generating set in the preset detection period can be used for other control processing of the wind generating set, and the description is omitted in the present disclosure.

On the other hand, if it is determined that the wind generating set operation data in the preset detection period has the equal interval distribution phenomenon, the method for detecting the fluctuation of the wind generating set operation data according to the embodiment of the present disclosure may return to step S501 to continue the operation data fluctuation detection.

According to the fluctuation detection method of the wind generating set operation data, not only can the problem that the real data fluctuation situation cannot be detected due to the fact that the statistic value is small due to the fact that the data fluctuation period is long be solved, but also short-time and accidental data jumping and interference can be automatically filtered, and the reliability of fluctuation detection is guaranteed. On the other hand, according to the fluctuation detection method of the wind generating set operation data, the detection accuracy is not influenced by the detection period and the data fluctuation amplitude, and therefore the method can be directly suitable for fluctuation detection of various types of data. In addition, according to the fluctuation detection method of the wind generating set operation data provided by the embodiment of the disclosure, no requirement is set on the detection threshold of the operation data, so that the parameter adjustment for the wind generating set is not required to be frequently performed. Meanwhile, the fluctuation detection method is simple and convenient to calculate and high in efficiency, can be directly realized in a PLC (programmable logic controller), and can ensure the detection accuracy.

Fig. 6 is a block diagram illustrating a fluctuation detection apparatus of wind turbine generator set operation data according to an exemplary embodiment of the present disclosure. The fluctuation detection device 600 of the wind generating set operation data can be implemented in the main controller of the wind generating set, and can also be implemented in any special controller in the wind generating set.

Referring to fig. 6, the apparatus 600 for detecting fluctuation of wind turbine generator system operation data may include a sampling unit 610, a calculating unit 620, a determining unit 630, and an output unit 640.

The sampling unit 610 may continuously sample wind turbine generator set operating data. As described above, the wind park operational data may be, for example, generator speed, pitch speed, generator torque, and the like.

The calculating unit 620 may calculate an average value of the running data of the wind generating set in the preset detection period. As described above, the preset detection period may include a plurality of sampling intervals for sampling wind turbine generator set operating data.

The determining unit 630 may determine whether the wind generating set operation data in the preset detection period has an equal interval distribution phenomenon based on the wind generating set operation data in the preset detection period and the calculated average value.

Specifically, the determining unit 630 may compare the wind turbine generator system operation data at each sampling time within the preset detection period with the calculated average value, and determine whether the wind turbine generator system operation data occurs an equidistant distribution phenomenon within the preset detection period based on the comparison result. Here, for any one sampling time within the preset detection period, the determining unit 630 may store a value of 1 in the memory in response to the wind turbine generator set operation data at any one sampling time being greater than the average value, and may store a value of 0 in the memory in response to the wind turbine generator set operation data at any one sampling time being not greater than the average value. Thereafter, the determining unit 630 may set a counter to count, and determine whether the wind generating set operation data is distributed at equal intervals within a preset detection period based on the count value of the counter. The counter may count according to the following rules: in response to the number of consecutively stored values 1 being greater than a first threshold value and then the number of consecutively stored values 0 being greater than the first threshold value, incrementing the count value of the counter by 1; in response to the number of consecutively stored values 0 being greater than a first threshold value and subsequently the number of consecutively stored values 1 being greater than a first predetermined threshold value, the count value of the counter is incremented by 1. Alternatively, in response to the count value of the counter being greater than the second threshold, the determining unit 630 may determine that the wind turbine generator set operation data is distributed at equal intervals within the preset detection period. According to an embodiment of the present disclosure, the first threshold may be an integer, for example, not less than 5, and the second threshold may be an integer, for example, not less than 2.

In addition, the counter can count according to the following rules: in response to the number of the consecutively stored values 1 being greater than a first threshold and the number of the subsequently consecutively stored values 0 being not greater than the first threshold, clearing the count value of the counter; in response to the number of consecutively stored values 0 being greater than the first threshold and then the number of consecutively stored values 1 being not greater than the first predetermined threshold, the count value of the counter is cleared.

In response to determining that the wind generating set operation data in the preset detection period are distributed at equal intervals, the output unit 640 may output information indicating that the wind generating set operation data in the preset detection period are subjected to sinusoidal fluctuation. For example, the output unit 640 may output a flag indicating that the wind turbine generator set operation data is subjected to sinusoidal fluctuation in a preset detection period, and a maximum value and a minimum value of the wind turbine generator set operation data in the preset detection period.

Referring to fig. 7, the controller 700 in the embodiment of the present disclosure may be a main controller of the wind power generation set, or may be any dedicated controller in the wind power generation set. The controller 700 disclosed according to the present embodiment may include a processor 710 and a memory 720. The 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. The memory 720 stores computer programs to be executed by the processor 710. Memory 720 includes high-speed random access memory and/or non-volatile computer-readable storage media. The method of fluctuation detection of wind park operational data as described above may be implemented when the processor 710 executes a computer program stored in the memory 720.

Alternatively, the controller 700 may communicate with various other components in the wind turbine generator set in wired/wireless communication, and may also communicate with other devices in the wind farm in wired/wireless communication. Further, the controller 700 may communicate with a device external to the wind farm in a wired/wireless communication manner.

The method for detecting fluctuation of wind turbine generator system operating data in the disclosed embodiments may be written as a computer program and stored on a computer-readable storage medium. The computer program, when executed by a processor, may implement the method of fluctuation detection of operational data as described above. Examples of computer-readable storage media 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, non-volatile memory, CD-ROM, CD-R, CD + R, CD-RW, CD + RW, DVD-ROM, DVD-R, DVD-RW, DVD + RW, DVD-RAM, BD-ROM, BD-R LTH, BD-RE, blu-ray or optical disk memory, hard Disk Drive (HDD), solid State Disk (SSD), card memory (such as a multimedia card, a Secure Digital (SD) card or an extreme digital (XD) card), magnetic tape, a floppy disk, a magneto-optical data storage device, an optical data storage device, a hard disk, a solid state disk, and any other device configured to store and provide computer programs and any associated data, data files and data structures in a non-transitory manner to a computer processor or computer such that the computer programs and any associated data processors are executed or computer programs. In one example, the computer program and any associated data, data files, and data structures are distributed over a network of 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 fashion by one or more processors or computers.

The fluctuation detection method and the fluctuation detection device for the wind generating set operation data in the embodiment of the disclosure can not only solve the problem that the actual data fluctuation cannot be detected due to a small statistical value caused by a long data fluctuation period, but also automatically filter short-time and accidental data jumps and interferences, and ensure the reliability of fluctuation detection. On the other hand, the fluctuation detection method and the fluctuation detection device for the wind generating set operation data in the embodiment of the disclosure have the advantages that the detection accuracy is not influenced by the detection period and the data fluctuation amplitude, so that the method and the device can be directly applied to the fluctuation detection of various types of data.

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

1. A method for detecting fluctuation of operating data of a wind generating set is characterized by comprising the following steps:

continuously sampling the operating data of the wind generating set;

calculating an average value of the running data of the wind generating set in a preset detection period;

determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the wind generating set operation data in the preset detection period and the average value;

and responding to the fact that the wind generating set operation data in the preset detection period are distributed at equal intervals, and outputting information indicating that the wind generating set operation data in the preset detection period are subjected to sine fluctuation.

2. The method of claim 1, wherein the preset detection period comprises a plurality of sampling intervals at which wind turbine generator set operating data is sampled.

3. The method of claim 1, wherein the step of determining whether the wind generating set operation data in the preset detection period has the equal interval distribution phenomenon comprises the following steps:

and comparing the running data of the wind generating set at each sampling moment in the preset detection period with the average value, and determining whether the running data of the wind generating set in the preset detection period are distributed at equal intervals or not based on the comparison result.

4. The method of claim 3, wherein the step of comparing the wind turbine generator system operating data at each sampling instant within the preset detection period to the average value comprises:

for any sampling moment in the preset detection period, responding to the fact that the running data of the wind generating set at any sampling moment is larger than the average value, and storing a value 1 into a memory;

and responding to the condition that the wind generating set operation data at any one sampling moment is not larger than the average value, and storing a value 0 into a memory.

5. The method of claim 4, wherein the step of determining whether the wind generating set operation data in the preset detection period has the equispaced distribution phenomenon based on the comparison result comprises the steps of:

setting a counter to count, wherein the counter counts according to the following rules:

in response to the number of consecutively stored values 1 being greater than a first threshold and subsequently the number of consecutively stored values 0 being greater than the first threshold, incrementing the count value of the counter by 1;

in response to the number of consecutively stored values 0 being greater than the first threshold and subsequently the number of consecutively stored values 1 being greater than the first predetermined threshold, incrementing the count value of the counter by 1;

and determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the count value of the counter.

6. The method of claim 5, wherein the step of determining whether the wind generating set operation data in the preset detection period has the equispaced distribution phenomenon based on the count value of the counter further comprises the steps of:

and determining that the wind generating set operation data in the preset detection period are distributed at equal intervals in response to the fact that the count value of the counter is larger than a second threshold value.

7. The method of claim 5, wherein the counter further counts according to the following rule:

in response to the number of consecutively stored values 1 being greater than a first threshold and the number of subsequently consecutively stored values 0 being not greater than the first threshold, clearing the count value of the counter;

in response to the number of consecutively stored values 0 being greater than the first threshold and the number of subsequently consecutively stored values 1 being not greater than the first predetermined threshold, clearing the count value of the counter.

8. The method of claim 1, wherein the step of outputting information indicating that the wind generating set operating data within the preset detection period fluctuates sinusoidally comprises:

and outputting a mark indicating that the wind generating set operation data in the preset detection period generate sine fluctuation and the maximum value and the minimum value of the wind generating set operation data in the preset detection period.

9. A fluctuation detection device for wind generating set operation data, characterized in that the device comprises:

a sampling unit configured to: continuously sampling the operating data of the wind generating set;

a computing unit configured to: calculating an average value of the running data of the wind generating set in a preset detection period;

a determination unit configured to: determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the wind generating set operation data in the preset detection period and the average value;

an output unit configured to: and responding to the fact that the wind generating set operation data in the preset detection period are distributed at equal intervals, and outputting information indicating that the wind generating set operation data in the preset detection period are subjected to sine fluctuation.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method for fluctuation detection of wind park operational data according to any one of claims 1 to 8.

11. A controller, characterized in that the controller comprises:

a processor; and

a memory storing a computer program which, when executed by the processor, implements the method of fluctuation detection of wind turbine generator set operational data according to any one of claims 1 to 8.

12. A wind park according to claim 11, wherein the wind park comprises a controller.