Method and device for detecting fluctuations in wind turbine operating data

Abstract

A fluctuation detection method and a fluctuation detection device for wind turbine generator system operation data are disclosed. The fluctuation detection method comprises: continuously sampling wind turbine generator system operation data; calculating an average value of the wind turbine generator system operation data in a preset detection period; determining whether the wind turbine generator system operation data in the preset detection period is equally spaced based on the wind turbine generator system operation data in the preset detection period and the average value; and in response to determining that the wind turbine generator system operation data in the preset detection period is equally spaced, outputting information indicating that the wind turbine generator system operation data in the preset detection period has a sinusoidal fluctuation.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of wind power generation in general, and more particularly, to a method and device for detecting fluctuation of wind turbine operation data. BACKGROUND

[0002] With the large-scale of wind turbine capacity, advanced wind power technologies such as variable pitch control and variable speed constant frequency have become the mainstream control method of current wind turbine. The generator is an important device in the wind turbine that converts wind energy into electrical energy, which directly affects the quality and efficiency of output power, and also affects the performance and complexity of the entire wind turbine.

[0003] The wind turbine is a relatively complex system. At present, the MW-level permanent magnet wind turbine highly integrates aerodynamics, structural mechanics, electromagnetism, material science, power electronics technology, power system analysis, relay protection technology, automatic control technology and modern communication, etc. comprehensive disciplines, becoming a complex energy conversion system, so the occurrence of the same fault may be caused by different reasons. For example, the "three-axis angle inconsistency" fault of the variable pitch system, the cause of the fault may be the encoder itself fault of the variable pitch system, the encoder power supply fault, the variable pitch system jammed, or the variable pitch drive fault.

[0004] At present, the fault detection of the wind turbine usually adopts simple numerical comparison, for example, when the speed is greater than a certain value, or the voltage of the backup power supply is lower than a certain value, the fault is triggered. However, after the components of the wind turbine fail, in addition to the numerical increase or decrease, in many cases, frequent data fluctuations will also occur and the wind turbine will recover to normal after shutdown. As a result, on the one hand, after the fault is reported, the maintenance personnel are not easy to find the cause of the fault. On the other hand, the timer in the controller has the characteristics that after the condition is turned on, the timer starts timing, and after the condition is turned off, the timer stops working and resets. Frequent fluctuations in data will also cause the timer to frequently start, reset, and fail to reach the timing time, resulting in the failure of the fault to trigger normally, making it difficult to troubleshoot the fault, and even if the fault cannot be triggered normally, it will also endanger the safety of the unit.

[0005] In order to analyze the fault causes of the wind turbine generator set, the operation data of the wind turbine generator set needs to be analyzed and diagnosed. The analysis and diagnosis can automatically, accurately and timely record the changes of various electrical quantities before and after the fault occurs. Through the analysis and comparison of the electrical quantities, the analysis and processing of the accident, the correct action of the protection and the safe and reliable operation of the wind turbine generator set play a very important role. In this analysis and diagnosis, whether the operation data has periodic and sinusoidal fluctuations is a key element for monitoring whether the wind turbine generator set is stable and normal. For example, if the variable pitch speed has sinusoidal and frequent fluctuations, the variable pitch motor will be adjusted and commutated too frequently, and then the temperature of the variable pitch motor will rise too fast. If the generator speed has sinusoidal and frequent fluctuations, the generator control is unstable and the generator is out of adjustment, which will affect the load and vibration of the wind turbine generator set. SUMMARY

[0006] Embodiments of the present disclosure provide a fluctuation detection method and a fluctuation detection device for wind turbine generator set operation data, which can be directly applied to the detection of various operation data, without the need to set detection period, detection amplitude and other parameters, and have wide applicability.

[0007] In one general aspect, there is provided a fluctuation detection method for wind turbine generator set operation data, the fluctuation detection method comprising: continuously sampling the wind turbine generator set operation data; calculating an average value of the wind turbine generator set operation data in a preset detection period; determining whether the wind turbine generator set operation data in the preset detection period has an equal-interval distribution phenomenon based on the wind turbine generator set operation data in the preset detection period and the average value; and in response to determining that the wind turbine generator set operation data in the preset detection period has the equal-interval distribution phenomenon, outputting information indicating that the wind turbine generator set operation data in the preset detection period has a sinusoidal fluctuation.

[0008] In another general aspect, there is provided a fluctuation detection device for wind turbine generator set operation data, the fluctuation detection device comprising: a sampling unit configured to continuously sample the wind turbine generator set operation data; a calculation unit configured to calculate an average value of the wind turbine generator set operation data in a preset detection period; a determination unit configured to determine whether the wind turbine generator set operation data in the preset detection period has an equal-interval distribution phenomenon based on the wind turbine generator set operation data in the preset detection period and the average value; and an output unit configured to, in response to determining that the wind turbine generator set operation data in the preset detection period has the equal-interval distribution phenomenon, output information indicating that the wind turbine generator set operation data in the preset detection period has a sinusoidal fluctuation.

[0009] In another general aspect, a computer readable storage medium storing a computer program is provided, which when executed by a processor, implements the wind turbine operating data fluctuation detection method as described above.

[0010] In another general aspect, a controller is provided, which includes: a processor; and a memory storing a computer program, which when executed by the processor, implements the wind turbine operating data fluctuation detection method as described above.

[0011] The wind turbine operating data fluctuation detection method and the fluctuation detection device in the embodiments of the present disclosure can not only solve the problem that the true data fluctuation cannot be detected due to the small statistical value caused by the long data fluctuation period, but also automatically filter out short-term and accidental data jumps and interference, thereby ensuring the reliability of the fluctuation detection. On the other hand, according to the wind turbine operating data fluctuation detection method and the fluctuation detection device in the embodiments of the present disclosure, the detection accuracy is not affected by the detection period or the data fluctuation amplitude, and thus the fluctuation detection method can be directly applied to the fluctuation detection of various types of data.

[0012] In addition, the wind turbine operating data fluctuation detection method and the fluctuation detection device in the embodiments of the present disclosure have no requirements for the detection threshold setting of the operating data, and thus do not need to frequently adjust the parameters of the wind turbine. Meanwhile, the fluctuation detection method is simple and efficient to calculate, can be directly implemented in a PLC controller, and can ensure the accuracy of the detection.

[0013] Aspects and / or advantages of the present general inventive concepts will become more fully apparent from the following description and accompanying drawings, in which: BRIEF DESCRIPTION OF DRAWINGS

[0014] The above and other objects and features of the present embodiments will become more apparent from the following description of embodiments taken in conjunction with the accompanying drawings, in which:

[0015] Figure 1 is a graph showing the generator speed as the object of fluctuation detection;

[0016] Figure 2 is a graph showing the pitch given speed and the pitch actual speed as the object of fluctuation detection;

[0017] Figure 3 is a diagram showing an example of the existing data fluctuation detection method;

[0018] Figure 4is 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;

[0019] Figure 5 is a flowchart illustrating a fluctuation detection method of wind turbine generator system operation data according to an embodiment of the present disclosure;

[0020] Figure 6 is a block diagram illustrating a fluctuation detection apparatus of wind turbine generator system operation data according to an exemplary embodiment of the present disclosure;

[0021] Figure 7 is a block diagram illustrating a controller according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] The following DETAILED DESCRIPTION is presented to help the reader understand the methods, apparatuses and / or systems described herein. However, the various changes, modifications and equivalents can be resorted to by those skilled in the art after understanding the disclosure presented herein. For example, the order of the operations presented in the methods described herein is merely exemplary and not limiting, and the operations can be changed, modified and / or reordered as will be apparent to those of ordinary skill in the art after having the benefit of the present disclosure. Furthermore, the descriptions of features known to those of ordinary skill in the art can be omitted in order to more clearly and concisely convey the subject matter of the present application.

[0023] At present, there are mainly the following methods for detecting periodic fluctuation of data.

[0024] One method is to consider that data fluctuation occurs when the data value is greater than a certain value for a single time or multiple times. However, this method cannot determine the timing of data jump. For example, data fluctuation refers to repeated jump of data in a short time, and only recording the jump can cause misjudgment due to short-time interference. In addition, it is not easy to determine the threshold for detecting the data value. For example, the normal value of the voltage of a super capacitor is 85V, and the voltage value greater than 91V is abnormal, but the maximum value of data jump can be 90V, which will cause missed detection. More importantly, when the data fluctuates periodically and sinusoidally, the data may not jump, and in this case, this method cannot detect the periodic sinusoidal fluctuation of the data.

[0025] Another method is to determine whether the data fluctuates by judging the slope of the data change. This method can detect whether the data normally rises or fluctuates, 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 data changing from 85V to 88V is almost equal to that of data changing from 85V to 91V. On the other hand, when the fluctuation period of the data is long, different detection times will lead to calculation deviation. If the detection period is too short, only the slope of single change can be detected, and the overall change of the data cannot be reflected; if the detection period is too long, the peak value can be skipped, resulting in detection error.

[0026] Another method is the variance method, and 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. In addition, variance cannot filter out false detection caused by accidental jump.

[0027] Figure 1 is a curve diagram showing the generator speed as the object of fluctuation detection. In Figure 1 , the abscissa represents the time value, and the ordinate represents the 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 operation data of the wind turbine generator set. In addition, during the periodic fluctuation of the generator speed, the generator speed generally shows a gradually decreasing trend. Therefore, if only the maximum and minimum values of the speed value are detected, since the generator speed will change with the change of the wind speed, the reference value is not fixed, and therefore it is difficult to effectively detect the fluctuation by using the threshold method.

[0028] Figure 2 is a curve diagram showing the pitch given speed and the pitch actual speed as the object of fluctuation detection. Figure 3 is a diagram showing an example of the existing data fluctuation detection method.

[0029] In Figure 2 and Figure 3 , the abscissa represents the time value, and the ordinate represents the speed value. It can be seen from Figure 2 that the pitch given speed 201 and the pitch actual speed 202 both fluctuate in a similar sinusoidal form. In combination with reference to Figure 3 , if the data change slope is used for data fluctuation detection, only the change slope within a certain range can be detected, and the detection accuracy depends on the fluctuation period of the data. However, the fluctuation period of the data is unknown in advance. As Figure 3As shown, in the interval t1-t2, the data change is very small, so if the detection period is very short, the detected curve change slope is very small, and the data fluctuation detection cannot be realized; and if the detection period is too long, for example Figure 3 As shown, in the interval t2-t3, the ordinate values at the two times are equivalent, and the detected curve change slope is still very small, which means that no data fluctuation occurs in the interval t2-t3, but in fact the data in this interval has an upward fluctuation. Therefore, using the data change slope to detect data fluctuation cannot accurately detect the data fluctuation trend.

[0030] On the other hand, if the variance value is used to detect data fluctuation, the detection accuracy also depends on the detection period. If the detection period is too short, the calculated variance value will be small, and if the detection period is too large, the calculated variance value depends on the amplitude of the data fluctuation. In addition, if the data has a short-time jump (for example, at time t4), the calculated variance value will also be large. Therefore, using the variance value to detect data fluctuation can only detect the degree of data deviation from the normal value, but cannot detect the trend of data fluctuation.

[0031] Figure 4 is a diagram showing the concept of a fluctuation detection method for wind turbine generator set operation data according to an embodiment of the present disclosure.

[0032] Referring to Figure 4 , the fluctuation detection method for wind turbine generator set operation data in the present embodiment calculates the average value of the operation data according to a certain detection period, and quantitatively processes the operation data according to whether the operation data at each sampling time is greater than the average value or less than or equal to the average value, and then determines the distribution curve of the operation data and the number of corresponding continuous operation data; if the distribution curve of the operation data presents periodic changes and the number of corresponding continuous operation data is large, it is considered that the operation data has a sinusoidal fluctuation. As shown Figure 4 By comparing the operation data at each sampling time with the average value, a distribution curve with high and low levels is generated. Since the size of the average value can change with the change of the operation data, and is not affected by the detection period, compared with the existing fluctuation detection method, this fluctuation detection method based on the data distribution curve is more reliable and accurate.

[0033] Figure 5 A flowchart of a fluctuation detection method for wind turbine generator set operation data according to an embodiment of the present disclosure is shown. The fluctuation detection method for wind turbine generator set operation data in the present embodiment can be implemented in the main controller of the wind turbine generator set, or in any dedicated controller in the wind turbine generator set.

[0034] Referring to Figure 5In step S501, the wind turbine operating data can be sampled continuously. Here, the wind turbine operating data can be, for example, generator speed, pitch speed, generator torque, etc. The wind turbine operating data can be acquired by various detection devices, and the present disclosure does not make any limitation in this regard.

[0035] In step S502, the average value of the wind turbine operating data in the preset detection period can be calculated. Here, the preset detection period can include a plurality of sampling intervals for sampling the wind turbine operating data. For example, the sampling interval can be 20 ms, and the preset detection period can be 500 ms. However, the above values are only examples, and the sampling interval and the length of the preset detection period can be adjusted appropriately as needed.

[0036] Next, in step S503, based on the wind turbine operating data in the preset detection period and the calculated average value, it can be determined whether the equal-interval distribution phenomenon of the wind turbine operating data in the preset detection period occurs.

[0037] Specifically, the wind turbine operating data at each sampling time in the preset detection period can be compared with the calculated average value, and based on the comparison result, it can be determined whether the equal-interval distribution phenomenon of the wind turbine operating data in the preset detection period occurs. When the wind turbine operating data at each sampling time in the preset detection period is compared with the calculated average value, if the wind turbine operating data at the sampling time is greater than the calculated average value, value 1 is stored in the memory, and if the wind turbine operating data at the sampling time is not greater than the calculated average value, value 0 is stored in the memory. The memory can be a memory in the controller running the operating fluctuation detection method, or other memory provided in the wind turbine. According to the embodiment of the present disclosure, the meaning of comparing the wind turbine operating data at each sampling time in the preset detection period with the calculated average value is that the average value can be automatically adjusted with the rising and falling trend of the operating data, thereby ensuring the detection accuracy.

[0038] After comparing the wind turbine generator set operation data at each sampling time within the preset detection period with the calculated average value, a counter can be set to count. Thereafter, whether the wind turbine generator set operation data within the preset detection period appears the equally spaced distribution phenomenon can be determined based on the count value of the counter. Here, the counter can be a memory in the controller of the operation fluctuation detection method, or a special counter provided in the wind turbine generator set. The set counter can count according to the following rules: if the number of continuously stored values 1 is greater than a first threshold value, and the number of subsequently continuously stored values 0 is greater than the first threshold value, the count value of the counter is increased by 1; if the number of continuously stored values 0 is greater than the first threshold value, and the number of subsequently continuously stored values 1 is greater than the first predetermined threshold value, the count value of the counter is increased by 1. Alternatively, if the count value of the counter is greater than a second threshold value, it can be determined that the wind turbine generator set operation data within the preset detection period appears the equally spaced distribution phenomenon. In the embodiments of the present disclosure, the first threshold value can be an integer not less than 5, and the second threshold value can be an integer not less than 2.

[0039] In addition, the counter can also count according to the following rules: if the number of continuously stored values 1 is greater than the first threshold value, and the number of subsequently continuously stored values 0 is not greater than the first threshold value, the count value of the counter is cleared; if the number of continuously stored values 0 is greater than the first threshold value, and the number of subsequently continuously stored values 1 is not greater than the first predetermined threshold value, the count value of the counter is cleared. Alternatively, according to the above rules, it can be determined that, in the case of clearing the counter, if the number of continuously stored values 1 or values 0 is not greater than the first threshold value, the count value of the counter will not increase.

[0040] If it is determined that the wind turbine generator set operation data within the preset detection period appears the equally spaced distribution phenomenon, in step S504, information indicating that the wind turbine generator set operation data within the preset detection period occurs the sine fluctuation can be output. According to the embodiments of the present disclosure, a flag indicating that the wind turbine generator set operation data within the preset detection period occurs the sine fluctuation and the maximum value and the minimum value of the wind turbine generator set operation data within the preset detection period can be output. Here, one specific implementation of outputting the flag indicating that the wind turbine generator set operation data within the preset detection period occurs the sine fluctuation is to output an alarm to prompt the operation and maintenance personnel to analyze the operation data and pay attention to the operation of the wind turbine generator set. In addition, the maximum value and the minimum value of the wind turbine generator set operation data within the preset detection period can be used for other control processing of the wind turbine generator set, which will not be described in detail herein.

[0041] On the other hand, if it is determined that the wind turbine generator set operation data within the preset detection period appears the equally spaced distribution phenomenon, the wind turbine generator set operation data fluctuation detection method according to the embodiments of the present disclosure can return to step S501 to continue the operation data fluctuation detection.

[0042] The fluctuation detection method of wind turbine generator set operation data according to the embodiments of the present disclosure can not only solve the problem that the statistical value is small and the real data fluctuation cannot be detected due to the long data fluctuation period, but also automatically filter out short-term and accidental data jumps and interference, thereby ensuring the reliability of the fluctuation detection. On the other hand, the fluctuation detection method of wind turbine generator set operation data according to the embodiments of the present disclosure has detection accuracy that is not affected by the detection period and is not affected by the data fluctuation amplitude, and thus can be directly applied to the fluctuation detection of various types of data. In addition, the fluctuation detection method of wind turbine generator set operation data according to the embodiments of the present disclosure has no requirements for the detection threshold setting of the operation data, and thus does not need to frequently adjust the parameters of the wind turbine generator set. At the same time, the fluctuation detection method is simple and efficient to calculate, can be directly implemented in a PLC controller, and can ensure the accuracy of the detection.

[0043] Figure 6 is a block diagram illustrating a fluctuation detection device of wind turbine generator set operation data according to an exemplary embodiment of the present disclosure. The fluctuation detection device 600 of wind turbine generator set operation data can be implemented in a main controller of a wind turbine generator set, or in any special-purpose controller in the wind turbine generator set.

[0044] Referring to Figure 6 , the fluctuation detection device 600 of wind turbine generator set operation data can include a sampling unit 610, a calculation unit 620, a determination unit 630, and an output unit 640.

[0045] The sampling unit 610 can continuously sample the wind turbine generator set operation data. As described above, the wind turbine generator set operation data can be, for example, generator speed, variable pitch speed, generator torque, etc.

[0046] The calculation unit 620 can calculate the average value of the wind turbine generator set operation data in a preset detection period. As described above, the preset detection period can include a plurality of sampling intervals in which the wind turbine generator set operation data is sampled.

[0047] The determination unit 630 can determine whether the wind turbine generator set operation data in the preset detection period appears to be equally spaced based on the wind turbine generator set operation data in the preset detection period and the calculated average value.

[0048] In particular, the determination unit 630 can compare the wind turbine generator set operation data at each sampling time within the preset detection period with the calculated average value, and determine whether the wind turbine generator set operation data within the preset detection period appears the equally-spaced distribution phenomenon based on the comparison result. Here, for any one sampling time within the preset detection period, the determination unit 630 can store value 1 into the memory in response to the wind turbine generator set operation data at the any one sampling time being greater than the average value, and can store value 0 into the memory in response to the wind turbine generator set operation data at the any one sampling time being not greater than the average value. Thereafter, the determination unit 630 can set a counter to count, and determine whether the wind turbine generator set operation data within the preset detection period appears the equally-spaced distribution phenomenon based on the count value of the counter. The counter can count according to the following rules: in response to the number of continuously stored values 1 being greater than a first threshold value, and the number of subsequently continuously stored values 0 being greater than the first threshold value, the count value of the counter is increased by 1; in response to the number of continuously stored values 0 being greater than the first threshold value, and the number of subsequently continuously stored values 1 being greater than the first predetermined threshold value, the count value of the counter is increased by 1. Alternatively, in response to the count value of the counter being greater than a second threshold value, the determination unit 630 can determine that the wind turbine generator set operation data within the preset detection period appears the equally-spaced distribution phenomenon. According to an embodiment of the present disclosure, the first threshold value can be an integer not less than 5, and the second threshold value can be an integer not less than 2.

[0049] In addition, the counter can also count according to the following rules: in response to the number of continuously stored values 1 being greater than the first threshold value, and the number of subsequently continuously stored values 0 being not greater than the first threshold value, the count value of the counter is cleared; in response to the number of continuously stored values 0 being greater than the first threshold value, and the number of subsequently continuously stored values 1 being not greater than the first predetermined threshold value, the count value of the counter is cleared.

[0050] In response to determining that the wind turbine generator set operation data within the preset detection period appears the equally-spaced distribution phenomenon, the output unit 640 can output information indicating that the wind turbine generator set operation data within the preset detection period occurs the sinusoidal fluctuation. For example, the output unit 640 can output a flag indicating that the wind turbine generator set operation data within the preset detection period occurs the sinusoidal fluctuation, and the maximum value and the minimum value of the wind turbine generator set operation data within the preset detection period.

[0051] Figure 7 is a block diagram illustrating a controller according to an embodiment of the present disclosure.

[0052] Referring to Figure 7The controller 700 in the embodiments of the present disclosure can be a main controller of the wind turbine generator or any special controller in the wind power generation group. The controller 700 disclosed in the embodiments can include a processor 710 and a memory 720. The processor 710 can 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 chip (SoC), a microprocessor, an application specific integrated circuit (ASIC), etc. The memory 720 stores a computer program to be executed by the processor 710. The memory 720 includes a high-speed random access memory and / or a non-volatile computer readable storage medium. When the processor 710 executes the computer program stored in the memory 720, the fluctuation detection method of the wind turbine generator operation data as described above can be implemented.

[0053] Alternatively, the controller 700 can communicate with other various components in the wind turbine generator in a wired / wireless communication manner, and can also communicate with other devices in the wind farm in a wired / wireless communication manner. In addition, the controller 700 can communicate with devices outside the wind farm in a wired / wireless communication manner.

[0054] The fluctuation detection method for the operating data of the wind turbine generator system 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 a processor, the fluctuation detection method for the operating data as described above can be implemented. Examples of the computer readable storage medium include a read only memory (ROM), a random access programmable read only memory (PROM), an electrically erasable programmable read only memory (EEPROM), a random access memory (RAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), a flash memory, a non-volatile memory, a CD-ROM, a CD-R, a CD+R, a CD-RW, a CD+RW, a DVD-ROM, a DVD-R, a DVD+R, a DVD-RW, a DVD+RW, a DVD-RAM, a BD-ROM, a BD-R, a BD-R LTH, a BD-RE, a Blu-ray or an optical disc memory, a hard disk drive (HDD), a solid state disk (SSD), a card memory (such as a multimedia card, a secure digital (SD) card or an extreme digital (XD) card), a 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 a computer program and any associated data, data files and data structures in a non-transitory manner and provide the computer program and any associated data, data files and data structures to a processor or a computer so that the processor or the computer can execute the computer program. In one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system, so that the computer program and any associated data, data files and data structures are stored, accessed and executed by one or more processors or computers in a distributed manner.

[0055] The fluctuation detection method and the fluctuation detection device for the operating data of the wind turbine generator system in the embodiments of the present disclosure can not only solve the case that a true data fluctuation cannot be detected due to a small statistical value caused by a long data fluctuation period, but also automatically filter out short-term and accidental data jumps and disturbances to ensure the reliability of the fluctuation detection. On the other hand, the fluctuation detection method and the fluctuation detection device for the operating data of the wind turbine generator system in the embodiments of the present disclosure have a detection accuracy that is not affected by the detection period and the data fluctuation amplitude, and thus can be directly applied to the fluctuation detection of various types of data.

[0056] In addition, the fluctuation detection method and the fluctuation detection device for the operating data of the wind turbine generator system in the embodiments of the present disclosure have no requirements for the detection threshold setting of the operating data, and thus do not need to frequently adjust the parameters of the wind turbine generator. At the same time, the fluctuation detection method is simple and efficient to calculate, can be directly implemented in a PLC controller, and can ensure the accuracy of the detection.

[0057] While certain embodiments of the disclosure have been shown and described, it is understood that modifications will occur to those skilled in the art, without departing from the spirit and scope of the disclosure, which is defined by the following claims and their equivalents.

Claims

1. A method of detecting fluctuations in wind turbine generator system operational data, the method comprising: The method comprises: sampling wind turbine operating data continuously; calculating an average value of the wind turbine operating data in a preset detection period; determining whether the wind turbine operating data in the preset detection period appears equal-interval distribution phenomenon based on the wind turbine operating data in the preset detection period and the average value; in response to determining that the wind turbine operating data in the preset detection period appears equal-interval distribution phenomenon, outputting information indicating that the wind turbine operating data in the preset detection period occurs sinusoidal fluctuation.

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

3. The method of claim 1, wherein, The step of determining whether the wind turbine operating data in the preset detection period appears equal-interval distribution phenomenon comprises: comparing the wind turbine operating data at each sampling time in the preset detection period with the average value, and determining whether the wind turbine operating data in the preset detection period appears equal-interval distribution phenomenon based on the comparison result.

4. The method of claim 3, wherein, The step of comparing the wind turbine operating data at each sampling time in the preset detection period with the average value comprises: in response to the wind turbine operating data at an arbitrary sampling time in the preset detection period being greater than the average value, storing value 1 into a memory; in response to the wind turbine operating data at the arbitrary sampling time not being greater than the average value, storing value 0 into the memory.

5. The method of claim 4, wherein, The step of determining whether the wind turbine operating data in the preset detection period appears equal-interval distribution phenomenon based on the comparison result comprises: setting a counter to count, wherein the counter counts according to the following rules: in response to the number of continuously stored value 1 being greater than a first threshold value, and the number of subsequently continuously stored value 0 being greater than the first threshold value, increasing the count value of the counter by 1; in response to the number of continuously stored value 0 being greater than the first threshold value, and the number of subsequently continuously stored value 1 being greater than the first threshold value, increasing the count value of the counter by 1; determining whether the wind turbine operating data in the preset detection period appears equal-interval distribution phenomenon based on the count value of the counter.

6. The method of claim 5, wherein, The step of determining whether the wind turbine operating data in the preset detection period appears equal-interval distribution phenomenon based on the count value of the counter further comprises: in response to the count value of the counter being greater than a second threshold value, determining that the wind turbine operating data in the preset detection period appears equal-interval distribution phenomenon.

7. The method of claim 5, wherein, The counter further counts according to the following rules: in response to the number of continuously stored value 1 being greater than a first threshold value, and the number of subsequently continuously stored value 0 not being greater than the first threshold value, clearing the count value of the counter; in response to the number of continuously stored value 0 being greater than the first threshold value, and the number of subsequently continuously stored value 1 not being greater than the first threshold value, clearing the count value of the counter.

8. The method of claim 1, wherein, The step of outputting information indicating that the wind turbine operating data in the preset detection period occurs sinusoidal fluctuation comprises: outputting a flag indicating that the wind turbine generator set operation data in the preset detection period has a sine fluctuation and maximum and minimum values of the wind turbine generator set operation data in the preset detection period.

9. A wind turbine generator system operation data fluctuation detection device characterized by, The device comprises: a sampling unit configured to continuously sample wind turbine generator set operation data; a calculation unit configured to calculate an average value of the wind turbine generator set operation data in a preset detection period; a determination unit configured to determine whether the wind turbine generator set operation data in the preset detection period has an equal-interval distribution phenomenon based on the wind turbine generator set operation data in the preset detection period and the average value; an output unit configured to output information indicating that the wind turbine generator set operation data in the preset detection period has a sine fluctuation in response to determining that the wind turbine generator set operation data in the preset detection period has an equal-interval distribution phenomenon.

10. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, implements the wind turbine generator set operation data fluctuation detection method according to any one of claims 1 to 8.

11. A controller characterized by comprising: The controller comprises: a processor; and a memory storing a computer program, which, when executed by the processor, implements the wind turbine generator set operation data fluctuation detection method according to any one of claims 1 to 8.

12. A wind power unit, characterized in that The wind turbine generator set comprises the controller according to claim 11. The computer program, when executed by a processor, implements the wind turbine generator set operation data fluctuation detection method according to any one of claims 1 to 8.

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