CN115523103A - Fluctuation detection method and fluctuation detection device for wind power generating set operation data - Google Patents

Abstract

Disclosed are a fluctuation detection method and a fluctuation detection device for operating data of a wind power generating set. The fluctuation detecting method includes: continuously sampling the operating data of the wind generating set; calculating the average value of the operating data of the wind generating set within a preset detection period; Based on the wind power generating set operating data in the preset detection period and the average value, determine whether the wind power generating set operating data in the preset detection period is distributed at equal intervals; in response to determining the preset detection period The operating data of the wind power generating set within a period is distributed at equal intervals, and information indicating that the operating data of the wind generating set within the preset detection period occurs sinusoidal fluctuations is output.

Description

Fluctuation detection method and fluctuation detection device for wind power generating set operation data

technical field

The present disclosure generally relates to the technical field of wind power generation, and more specifically, relates to a fluctuation detection method and a fluctuation detection device for operating data of a wind power generating set.

Background technique

With the increasing capacity of wind turbines, advanced wind power technologies such as variable pitch control and variable speed constant frequency have become the mainstream control methods for wind turbines. The generator is an important device for converting wind energy into electrical energy in a wind turbine. It not only directly affects the quality and efficiency of the output electric energy, but also affects the performance and structure complexity of the entire wind turbine.

A wind turbine is a relatively complex system. At present, MW-level permanent magnet wind turbines are highly integrated with comprehensive disciplines such as aerodynamics, structural mechanics, electrical machinery, material science, power electronics technology, power system analysis, relay protection technology, automatic control technology and modern communications, and become a set of comprehensive disciplines. Complex energy conversion system, so the occurrence of the same failure may 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 the fault of the encoder itself of the pitch system, the fault of the encoder power supply, the jamming of the pitch system, or the Pitch drive failure.

At present, the fault detection of wind turbines usually uses pure numerical comparison, for example, when the speed is greater than a certain value, or the voltage of the backup power source is lower than a certain value, a fault is triggered. However, after a component failure of the wind turbine, in addition to the increase or decrease of the value, frequent data fluctuations will occur in many cases and the wind turbine will return to normal after the shutdown. As a result, on the one hand, after the fault is reported, it is difficult for the operation and maintenance personnel to find the cause of the fault. On the other hand, the characteristic of the timer in the controller is that when the condition is turned on, the timer starts counting, and when the condition is turned off, the timer stops working and resets, and the frequent fluctuation of data will also cause the timer to start and reset frequently, However, the timing cannot be reached, causing the failure to be triggered normally, causing difficulties in troubleshooting, and even, if the failure cannot be triggered normally, it will also endanger the safety of the unit.

In order to analyze the cause of the failure of the wind turbine, it is necessary to analyze and diagnose the operating data of the wind turbine. This analysis and diagnosis can automatically, accurately and timely record the changes of various electrical quantities in the process before and after the failure at the time of the failure. Through the analysis and comparison of these electrical quantities, it plays a very important role in analyzing and handling accidents, judging whether the protection is operating correctly, and the safe and reliable operation of the wind turbine. In this kind of analysis and diagnosis, judging whether the operation data has periodic and sinusoidal fluctuations is the key element to monitor whether the operation of the wind turbine is stable and normal. For example, if the pitching speed fluctuates sinusoidally and frequently, it will cause the pitch motor to be adjusted too frequently due to pitch adjustment and commutation, which will cause the temperature of the pitch motor to rise too fast; if the generator speed fluctuates sinusoidally, Frequent fluctuations indicate unstable generator control and generator imbalance, which will affect the load and vibration of the wind turbine.

Contents of the invention

The embodiments of the present disclosure provide a fluctuation detection method and a fluctuation detection device for wind power generator operating data, which can be directly applied to the detection of various operating data without setting parameters such as detection period and detection amplitude, and have wide applicability .

In a general aspect, a method for detecting fluctuations in the operating data of a wind power generating set is provided. The fluctuation detecting method includes: continuously sampling the operating data of a wind generating set; Average value; based on the wind power generating set operating data in the preset detection period and the average value, determine whether the wind power generating set operating data in the preset detection period has an equidistant distribution phenomenon; in response to determining the predetermined Assuming that the operating data of the wind power generating set within the detection period is distributed at equal intervals, output information indicating that the operating data of the wind generating set within the preset detection period has sinusoidal fluctuations.

In another general aspect, a device for detecting fluctuations in the operation data of a wind power generating set is provided. The device for detecting fluctuations includes: a sampling unit configured to: continuously sample the operating data of a wind power generating set; a computing unit configured to is: calculating the average value of the wind power generating set operating data within the preset detection period; the determination unit is configured to: determine the preset Whether the operation data of the wind power generating set within the detection period is distributed at equal intervals; the output unit is configured to: in response to determining that the operation data of the wind power generating set within the preset detection cycle has an equal interval distribution, output an indication of the predetermined It is assumed that the wind turbine operating data within the detection period has sinusoidal fluctuation information.

In another general aspect, a computer-readable storage medium storing a computer program is provided, and when the computer program is executed by a processor, the above-mentioned method for detecting fluctuations in operating data of a wind power generating set is realized.

In another general aspect, there is provided a controller comprising: a processor; and a memory storing a computer program, which, when executed by the processor, implements the operation of the wind turbine as described above Data fluctuation detection method.

The fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure can not only solve the situation that the statistical value is small due to the long data fluctuation period and cannot detect the real data fluctuation, but also can automatically filter out short Timely and occasional data jumps and interferences ensure the reliability of fluctuation detection. On the other hand, according to the fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure, the detection accuracy is not affected by the detection period, nor is it affected by the data fluctuation amplitude, so it can be directly applied to Fluctuation detection for many types of data.

In addition, the fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure do not require any detection threshold setting of the operating data, so frequent parameter adjustments for the wind generating set are not required. At the same time, the fluctuation detection method is simple and efficient in calculation, can be directly implemented in a PLC controller, and can ensure detection accuracy.

Additional aspects and/or advantages of the general inventive concept of the present disclosure will be partially set forth in the following description, and some of them will be clear from the description, or can be learned through implementation of the general inventive concept of the present disclosure.

Description of drawings

The above and other objects and features of the embodiments of the present disclosure will become more apparent through the following description in conjunction with the accompanying drawings showing the embodiments, wherein:

FIG. 1 is a graph showing a rotational speed of a generator as an object of fluctuation detection;

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

3 is a diagram illustrating an example of an existing data fluctuation detection method;

FIG. 4 is a diagram illustrating the concept of a method for detecting fluctuations in operating data of a wind power generating set according to an embodiment of the present disclosure;

Fig. 5 is a flow chart showing a method for detecting fluctuations in wind power generating set operating data according to an embodiment of the present disclosure;

Fig. 6 is a block diagram showing a fluctuation detection device for wind power generating set operating data according to an exemplary embodiment of the present disclosure;

FIG. 7 is a block diagram illustrating a controller in an embodiment according to the present disclosure.

detailed description

The following detailed description is provided to assist the reader in gaining an overall 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 after understanding the disclosure of the present application. For example, the order of operations described herein are examples only, and are not limited to those orders set forth herein, but, except for operations that must occur in a particular order, may occur as will become apparent after understanding the disclosure of this application. That's changed. Also, descriptions of features that are known in the art may be omitted for increased clarity and conciseness.

At present, there are mainly the following methods for detecting periodic fluctuations in data.

One method is to judge that the data fluctuates when it is judged that the data value is greater than a certain value for one time or multiple times. However, this method cannot judge the timing of data transitions. For example, data fluctuation means that the data jumps repeatedly in a short period of time, and only the jumps are recorded, which may cause misjudgment due to short-term interference. In addition, it is not easy to determine the threshold value for the detection data value. For example, the normal value of the voltage of the supercapacitor is 85V, and the voltage value greater than 91V is abnormal, but the maximum value of the data jump may be 90V, which will cause missed detection. More importantly, when the data has periodic sinusoidal fluctuations, the data does not necessarily appear to jump. In this case, the method cannot detect the periodic sinusoidal fluctuations of the data.

Another method is to determine whether the data fluctuates by judging the slope of the data change. This method can detect whether the data is rising normally or fluctuating to a certain extent, but it cannot accurately reflect the fluctuation range of the data, especially the fluctuation period of the data. For example, the slope of data changing from 85V to 88V is almost the same as that of data changing from 85V to 91V. On the other hand, when the fluctuation period of the data is long, the difference in detection time will lead to calculation deviation. If the detection period is too short, only the slope of a single change is detected, which cannot reflect the overall change of the data; if the detection period is too long, the peak may be skipped, resulting in detection errors.

Another method is the variance method. The detection result of this method also depends on the detection period. On the other hand, this method can only detect whether the data deviates from the normal value, but cannot detect the fluctuation trend of the data. Furthermore, variance cannot filter out false detections caused by occasional transitions.

FIG. 1 is a graph showing a generator rotation speed as an object of fluctuation detection. In FIG. 1, the abscissa represents the time value, and the ordinate represents the rotational speed value. It can be seen from Figure 1 that the generator speed (also the impeller speed) fluctuates in a sinusoidal form, which belongs to the abnormal fluctuation phenomenon of the wind turbine operating data. In addition, during the periodic fluctuation of the generator speed, the generator speed generally shows a gradual downward trend. Therefore, if only the maximum and minimum values of the rotational speed are detected, the reference value of the generator rotational speed is not fixed because the rotational speed of the generator changes with the change of the wind speed, so it is difficult to effectively detect fluctuations by using the threshold method.

FIG. 2 is a graph showing a pitch given speed and a pitch actual speed that are objects of fluctuation detection. FIG. 3 is a diagram showing an example of an existing data fluctuation detection method.

In Fig. 2 and Fig. 3, the abscissa represents the time value, and the ordinate represents the speed value. It can be seen from FIG. 2 that both the given pitch speed 201 and the actual pitch speed 202 fluctuate in a sinusoidal form. With reference to FIG. 3 , if the data variation slope is used for data fluctuation detection, only the variation slope within a certain range can be detected, and the detection accuracy depends on the data fluctuation period. However, the fluctuation period of the data is unknown in advance. As shown in Figure 3, in the interval between t1 and t2, the amount of data change is very small, so if the detection period is very short, the slope of the detected curve change is very small, and the detection of data fluctuations cannot be realized; and if the detection period is too long, for example, as shown in Figure 3 In the interval from t2 to t3 shown in 3, the ordinate values at the two moments are equivalent, and the slope of the detected curve change is still very small, which means that there is no data fluctuation in the interval from t2 to t3, but in fact, the data in this interval has an upward trend. A case of increased volatility. Therefore, using the data change slope to detect data fluctuations cannot accurately detect data fluctuation trends.

On the other hand, if the variance value is used for data fluctuation detection, the detection accuracy also depends on the detection cycle. If the detection cycle is too short, the calculated variance value will be small, and if the detection cycle is too large, the calculated variance value will It also depends on the magnitude of data fluctuations. In addition, if the data has a short-term jump (for example, at time t4), the calculated variance value will also be large. Therefore, using the variance value to detect data fluctuations can only detect the degree to which the data deviates from the normal value, but cannot detect the trend of data fluctuations.

Fig. 4 is a diagram illustrating the concept of a method for detecting fluctuations in wind power generating set operating data according to an embodiment of the present disclosure.

Referring to FIG. 4 , the method for detecting fluctuations in the operating data of wind power generators in the embodiments of the present disclosure calculates the average value of the operating data according to a certain detection period, and performs quantification processing 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 determine the distribution curve of the operating data and the number of corresponding continuous operating data; if the distribution curve of the operating data shows periodic changes, and the number of corresponding continuous operating data is large, it is considered that the operating data has sinusoidal fluctuations. As shown in Figure 4, by comparing the operating data at each sampling time with the average value, a distribution curve with alternating high and low is generated. Because the size of the average value can change with the change of the operating data without being affected by the detection period, this method of fluctuation detection based on the data distribution curve is more reliable and accurate than the existing fluctuation detection methods.

Fig. 5 shows a flow chart of a method for detecting fluctuations in operating data of a wind power generating set according to an embodiment of the present disclosure. The method for detecting fluctuations in the operating data of the wind power generating set in the embodiments of the present disclosure may be implemented in the main controller of the wind generating set, or in any dedicated controller in the wind generating set.

Referring to Fig. 5, in step S501, the operation data of the wind power generating set may be continuously sampled. Here, the operating data of the wind power generating set may be, for example, the rotational speed of the generator, the pitch speed, the torque of the generator, and the like. The operation data of the wind power generating set can be acquired through various detection devices, which is not limited in this disclosure.

In step S502, the average value of the wind power generating set operating data within a preset detection period may be calculated. Here, the preset detection period may include a plurality of sampling intervals for sampling the operation data of the wind power generating set. For example, the sampling interval may be 20ms, and the preset detection period may be 500ms. However, the above numerical values are only examples, and the sampling interval and the duration of the preset detection period can be adjusted appropriately as required.

Next, in step S503, based on the operating data of the wind generating set within the preset detecting period and the calculated average value, it is determined whether the operating data of the wind generating set within the preset detecting period is distributed at equal intervals.

Specifically, the operating data of the wind power generating set at each sampling moment in the preset detection period can be compared with the calculated average value, and based on the comparison result, it can be determined whether there is an equidistant distribution phenomenon in the operating data of the wind power generating set in the preset detection period . When comparing the operating data of the wind power generating set at each sampling moment within the preset detection period with the calculated average value, if the operating data of the wind generating set at the sampling moment is greater than the calculated average value, store the value 1 into the memory , if the running data of the wind power generating set at the sampling moment is not greater than the calculated average value, store the value 0 into the memory. The memory may be a memory in the controller running the fluctuation detection method, or other memory provided in the wind power generating set. According to the embodiment of the present disclosure, the significance of comparing the operating data of the wind power generating set at each sampling moment within the preset detection period with the calculated average value is that the average value can be automatically adjusted along with the rising and falling trends of the operating data, Thereby ensuring the detection accuracy.

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

In addition, the counter can also count according to the following rules: if the number of continuously stored value 1 is greater than the first threshold, and the number of subsequently continuously stored value 0 is not greater than the first threshold, then the count value of the counter is cleared; If the number of stored values of 0 is greater than the first threshold, and the number of subsequently continuously stored values of 1 is not greater than the first predetermined threshold, the count value of the counter is cleared to zero. Optionally, it may be determined according to the above rule that, in the case of the counter being cleared, if the number of consecutively stored values of 1 or 0 is not greater than the first threshold, the count value of the counter will not increase.

If it is determined that the operating data of the wind power generating set within the preset detection period is distributed at equal intervals, then in step S504, information indicating that the operating data of the wind power generating set within the preset detection period has sinusoidal fluctuations may be output. According to an embodiment of the present disclosure, a flag indicating sinusoidal fluctuation of the wind power generating set operating data within a preset detection period and the maximum and minimum values of the wind power generating set operating data within the preset detection period may be output. Here, a specific implementation manner of outputting a flag indicating sinusoidal fluctuations in the operating data of the wind power generating set within a preset detection period is to output an alarm to prompt the operation and maintenance personnel to analyze the operating data and pay attention to the operation of the wind generating set. In addition, the maximum and minimum values of the operating data of the wind power generating set within the preset detection period can be used for other control processing of the wind power generating set, which will not be repeated in this disclosure.

On the other hand, if it is determined that the operating data of the wind power generating set within the preset detection period is distributed at equal intervals, the method for detecting fluctuations in the operating data of the wind generating set according to the embodiment of the present disclosure may return to step S501 and continue to detect fluctuations in the operating data .

According to the method for detecting fluctuations in the operating data of wind power generating sets in the embodiments of the present disclosure, it can not only solve the problem that the real data fluctuations cannot be detected due to the small statistical value due to the long data fluctuation period, but also automatically filter out short-term and accidental data Jump and interference, to ensure the reliability of fluctuation detection. On the other hand, according to the method for detecting fluctuations in the operating data of wind power generating sets in the embodiments of the present disclosure, the detection accuracy is not affected by the detection period, nor is it affected by the data fluctuation amplitude, so it can be directly applied to the detection of various types of data. Fluctuation detection. In addition, according to the method for detecting fluctuations in the operating data of the wind power generating set according to the embodiments of the present disclosure, there is no requirement for setting the detection threshold of the operating data, so frequent parameter adjustments for the wind generating set are not required. At the same time, the fluctuation detection method is simple and efficient in calculation, can be directly implemented in a PLC controller, and can ensure detection accuracy.

Fig. 6 is a block diagram illustrating an apparatus for detecting fluctuations in operating data of a wind power generating set according to an exemplary embodiment of the present disclosure. The fluctuation detection device 600 for the operation data of the wind power generating set can be implemented in the main controller of the wind generating set, or in any dedicated controller in the wind generating set.

Referring to FIG. 6 , an apparatus 600 for detecting fluctuations in operating data of a wind power generating set may include a sampling unit 610 , a calculation unit 620 , a determination unit 630 and an output unit 640 .

The sampling unit 610 can continuously sample the operation data of the wind power generating set. As mentioned above, the wind turbine operating data may be, for example, generator speed, pitch speed, generator torque, and the like.

The calculation unit 620 can calculate the average value of the wind power generating set operating data within a preset detection period. As mentioned above, the preset detection period may include a plurality of sampling intervals for sampling the operation data of the wind power generating set.

The determining unit 630 may determine whether the wind power generating set operating data in the preset detecting period is distributed at equal intervals based on the operating data of the wind generating set in the preset detecting period and the calculated average value.

Specifically, the determination unit 630 can compare the operating data of the wind power generating set at each sampling moment in the preset detection period with the calculated average value, and determine whether the operating data of the wind power generating set in the preset detection period occurs or not based on the comparison result. Interval distribution phenomenon. Here, for any sampling time within the preset detection period, the determination unit 630 may store the value 1 into the memory in response to the wind power generating set operating data at any sampling time being greater than the average value, and may respond to any sampling time The running data of the wind power generating set is not greater than the average value, and the value 0 is stored in the memory. Afterwards, the determination unit 630 may set a counter to count, and determine whether the operation data of the wind power generating set has an equidistant distribution phenomenon 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 of 1 being greater than a first threshold, and subsequently the number of consecutively stored values of 0 being greater than the first threshold, the count value of the counter is increased by 1; in response to the continuously stored value The number of 0's is greater than the first threshold, and subsequently the number of consecutively stored 1's is greater than the first predetermined threshold, incrementing the count value of the counter by one. Optionally, in response to the count value of the counter being greater than the second threshold, the determining unit 630 may determine that the operating data of the wind power generating set has an equidistant distribution phenomenon within a preset detection period. According to an embodiment of the present disclosure, the first threshold may be, for example, an integer not less than 5, and the second threshold may be, for example, an integer not less than 2.

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

In response to determining that the operating data of the wind power generating set has an equidistant distribution phenomenon within the preset detection period, the output unit 640 may output information indicating that the operating data of the wind power generating set has sinusoidal fluctuations within the preset detection period. For example, the output unit 640 may output a flag indicating sinusoidal fluctuations in the operating data of the wind power generating set within a preset detection period, and the maximum and minimum values of the operating data of the wind power generating set within the preset detection period.

FIG. 7 is a block diagram illustrating a controller in an embodiment according to the present disclosure.

Referring to FIG. 7 , the controller 700 in the embodiment of the present disclosure may be the main controller of the wind power generating set, or any dedicated controller in the wind generating set. The controller 700 disclosed according to this 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 so on. 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. When the processor 710 executes the computer program stored in the memory 720, the method for detecting fluctuations in the operation data of the wind power generating set as described above can be realized.

Optionally, the controller 700 may communicate with various other components in the wind power generating set in a wired/wireless communication manner, and may also communicate with other devices in the wind farm in a wired/wireless communication manner. In addition, the controller 700 may communicate with devices outside the wind farm in a wired/wireless communication manner.

The method for detecting fluctuations in the operating data of a wind power generating set in the embodiments of the present disclosure can be written as a computer program and stored on a computer-readable storage medium. When the computer program is executed by the processor, the method for detecting fluctuations in operating data as described above can be realized. 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, 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 Disc storage, Hard Disk Drive (HDD), Solid State Drive ( SSD), memory cards (such as Multimedia Cards, Secure Digital (SD) or Extreme Digital (XD) cards), magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state disks, and any other device , said any other means configured to store in a non-transitory manner a computer program and any associated data, data files and data structures and to provide said computer program and any associated data, data files and data structures to a processing A processor or computer enables a processor or computer to execute the computer program. In one example, the computer program and any associated data, data files and data structures are distributed over a networked computer system such that the computer program and any associated data, data files and data structures are processed by one or more processors or Computers store, access and execute in a distributed fashion.

The fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure can not only solve the situation that the statistical value is small due to the long data fluctuation period and cannot detect the real data fluctuation, but also can automatically filter out short Timely and occasional data jumps and interferences ensure the reliability of fluctuation detection. On the other hand, the detection accuracy of the fluctuation detection method and fluctuation detection device for the wind power generating set operating data in the embodiment of the present disclosure is not affected by the detection period, nor is it affected by the data fluctuation amplitude, so it can be directly applied to multiple Fluctuation detection for various types of data.

In addition, the fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure do not require any detection threshold setting of the operating data, so frequent parameter adjustments for the wind generating set are not required. At the same time, the fluctuation detection method is simple and efficient in calculation, can be directly implemented in a PLC controller, and can ensure detection accuracy.

While certain embodiments of the present disclosure have been shown and described, it should be understood by those skilled in the art that modifications may be made to these embodiments without departing from the principles and spirit of the disclosure, the scope of which is defined by the claims and their equivalents. to modify.

Claims (12)

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.

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