A wind turbine yaw angle-to-wind angle anomaly intelligent early warning method, system, device and medium

Abstract

The application provides a wind turbine yaw angle of wind abnormality intelligent early warning method, system, device and medium, which comprises the following steps: step 1, the historical data of the target unit is obtained and pretreated to obtain a matrix, wherein the first column of the matrix is the wind speed, the second column is the angle of wind, and the third column is the active power; step 2, the active power is divided into multiple wind speed-angle compartments by using the wind speed and the angle of wind; step 3, a two-dimensional matrix and a three-dimensional matrix are respectively constructed according to the multiple wind speed-angle compartments; step 4, whether the dynamic deviation of the angle of wind is normal is judged by using the two-dimensional matrix; whether the operation of the target unit is normal is judged by using the three-dimensional matrix, wherein if the operation of the unit is abnormal, a four-dimensional matrix is constructed by using the three-dimensional matrix, and whether the static deviation of the angle of wind is normal is judged by using the four-dimensional matrix; and the application can more comprehensively and accurately judge the yaw angle of wind abnormality of the unit.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine pitch motor fault early warning technology, specifically involving an intelligent early warning method, system, equipment and medium for abnormal yaw angle of wind turbine units. Background Technology

[0002] In recent years, intelligent operation and maintenance of wind farms has become a hot topic in the industry. However, in the process of intelligent operation and maintenance of wind farms, it is particularly important to be able to provide early warnings of equipment failures through various intelligent algorithms. Whether the yaw of the unit is abnormal is related to the absorption of wind energy by the unit and directly affects the power generation of the unit. Moreover, abnormal dynamic deviation of the unit's yaw angle will increase the load on the unit, and prolonged operation with defects will lead to a reduction in the lifespan of the unit.

[0003] Currently, while many different methods have been proposed domestically and internationally for early warning of abnormal wind turbine angles, most are only applied to a small number of units offline. They have poor fault tolerance for data collection and reading from large numbers of units, and online applications can affect the accuracy of algorithm model calculations, potentially leading to model crashes, disconnections, and inoperability. Current methods for judging abnormal wind turbine angles rarely focus on dynamic deviation angles, and the calculation of static deviation angles typically involves examining the power output at different wind angles under a specified wind speed, or analyzing the power curves at different wind angles to determine which curve is at the top. The first method only utilizes a small portion of the turbine data, failing to fully exploit its potential, resulting in lower accuracy. The second method currently does not address the issue of power curve intersections, making it impossible to automatically and accurately calculate the wind angle deviation. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent early warning method, system, device and medium for abnormal yaw angle of wind turbine units, which solves the defect that the existing calculation of wind angle has a large deviation, resulting in inaccurate judgment results of abnormal wind angle.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] This invention provides an intelligent early warning method for abnormal yaw angle of wind turbine generators, comprising the following steps:

[0007] Step 1: Preprocess the historical data of the target unit to obtain a matrix, where the first column of the matrix is ​​wind speed, the second column is the wind angle, and the third column is active power.

[0008] Step 2: Divide the active power into compartments using wind speed and wind angle to obtain multiple wind speed-angle compartments;

[0009] Step 3: Construct two-dimensional and three-dimensional matrices based on the obtained multiple wind speed-angle chambers;

[0010] Step 4: Use the obtained two-dimensional matrix to determine whether the dynamic deviation of the wind angle is normal; use the three-dimensional matrix to determine whether the target unit is operating normally. If the unit is operating abnormally, use the three-dimensional matrix to construct a four-dimensional matrix and use the four-dimensional matrix to determine whether the static deviation of the wind angle is normal.

[0011] Preferably, in step 1, the historical data of the acquired target unit is preprocessed to obtain a matrix. Specifically, the method is as follows:

[0012] S11, the historical data includes unit status, wind speed, active power, pitch angle and wind angle; obtain the configuration parameters of the target unit, the configuration parameters include cut-in wind speed, rated wind speed and rated power;

[0013] S12: Select data from historical data that are greater than the cut-in wind speed but less than the rated wind speed, have active power greater than zero, have the unit in normal power generation status, and have a pitch angle less than 2, and form a matrix.

[0014] Preferably, in step 2, the active power is divided into bins with a width of 1 using wind speed and wind angle to obtain multiple wind speed-angle bins.

[0015] Preferably, in step 3, a two-dimensional matrix is ​​constructed based on the obtained multiple wind speed-angle compartments. The specific method is as follows:

[0016] The active power data corresponding to each wind angle in the chamber is statistically analyzed for each wind speed-angle, and the wind angle and active power data are combined to form a two-dimensional matrix.

[0017] Preferably, in step 3, the obtained two-dimensional matrix is ​​used to determine whether the dynamic deviation of the wind angle is normal. The specific method is as follows:

[0018] Obtain the windward angle corresponding to the largest data volume from all data volumes in the two-dimensional matrix;

[0019] If the absolute value of the windward angle is greater than or equal to the set threshold, the dynamic deviation of the windward angle of the target unit is abnormal; otherwise, the dynamic deviation of the windward angle of the target unit is normal.

[0020] Preferably, in step 3, a three-dimensional matrix is ​​constructed based on the obtained multiple wind speed-angle chambers. The specific method is as follows:

[0021] Calculate the amount of active power data in the chamber for each wind speed and angle.

[0022] Delete the wind speed-angle compartments whose data volume is less than the set value to obtain the remaining wind speed-angle compartments;

[0023] Calculate the power mean among all active power values ​​corresponding to each remaining wind speed-angle compartment;

[0024] The average wind speed, wind angle, and power values ​​in all remaining wind speed-angle chambers are combined to form a three-dimensional matrix;

[0025] In step 3, a four-dimensional matrix is ​​constructed using a three-dimensional matrix. The specific method is as follows:

[0026] Obtain the average maximum power value corresponding to each wind speed from the three-dimensional matrix;

[0027] Obtain the windward angle corresponding to the average maximum power;

[0028] Obtain the average power values ​​for each wind speed when the wind angle is zero from the three-dimensional matrix, and calculate the average value corresponding to all power values.

[0029] Calculate the power difference between the mean and the maximum power for each maximum power;

[0030] Obtain the sum of the average power data for each wind speed;

[0031] The wind speed and wind angle in the three-dimensional matrix, along with the sum of the calculated data and the power difference, are combined to form a four-dimensional matrix.

[0032] Preferably, in step 3, a four-dimensional matrix is ​​used to determine whether the static deviation of the wind angle is normal. The specific method is as follows:

[0033] Calculate the mean power difference for all power differences;

[0034] The average power difference is compared with a set threshold. If the average power difference is greater than the set threshold, the static deviation of the wind angle is calculated using a four-dimensional matrix; otherwise, the target unit is operating normally.

[0035] The absolute value of the obtained static deviation of the wind angle is compared with the threshold. If the absolute value of the static deviation of the wind angle is greater than the threshold, the static deviation of the wind angle is abnormal; otherwise, the static deviation of the wind angle is normal.

[0036] An intelligent early warning system for abnormal yaw angle of a wind turbine includes:

[0037] The data acquisition unit is used to preprocess the historical data of the acquired target unit to obtain a matrix, wherein the first column of the matrix is ​​wind speed, the second column is the wind angle, and the third column is active power.

[0038] The compartmentalization unit is used to divide active power into compartments based on wind speed and wind angle, resulting in multiple wind speed-angle compartments.

[0039] Matrix construction unit, used to construct two-dimensional and three-dimensional matrices respectively based on the obtained multiple wind speed-angle compartments.

[0040] The anomaly detection unit is used to determine whether the dynamic deviation of the wind angle is normal using the obtained two-dimensional matrix; and to determine whether the target unit is operating normally using the three-dimensional matrix. If the unit is operating abnormally, a four-dimensional matrix is ​​constructed using the three-dimensional matrix, and the four-dimensional matrix is ​​used to determine whether the static deviation of the wind angle is normal.

[0041] A processing apparatus comprising at least a processor and a memory, wherein a computer program is stored in the memory, and the processor executes steps to implement the method when running the computer program.

[0042] A computer storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to perform the steps according to the method.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] This invention provides an intelligent early warning method for abnormal yaw angle of wind turbine units. It calculates both the static and dynamic deviations of the wind angle, enabling a more comprehensive and accurate identification of abnormal yaw angles. In the static deviation judgment process, a four-dimensional matrix is ​​constructed using wind speed, wind angle, the sum of data, and power difference. This matrix is ​​then used to calculate the static deviation. This process incorporates the unit's operating mechanism; for example, during the power ramp-up phase, with blade length, air density, and the main control program remaining constant, the unit's output power is primarily related to wind speed and wind angle. The invention employs a multi-parameter automatic weighting method, which not only effectively calculates the static deviation of the wind angle but also avoids extensive parameter adjustments in the later stages of calculation, saving significant manpower and time, thereby improving the accuracy of the abnormal wind angle judgment results.

[0045] Furthermore, this invention preprocesses the historical data of the target unit, enabling the method to be applied to online early warning of a large number of units, thus providing a certain degree of fault tolerance and greatly avoiding the problem of abnormal calculation results of the algorithm model caused by abnormal data acquisition and reading issues encountered in the application of a large number of units.

[0046] The intelligent early warning system for abnormal yaw angle of wind turbine provided by this invention calculates the static and dynamic deviations of the wind angle, which can more comprehensively and accurately identify the problem of abnormal yaw angle of the turbine. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0048] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0049] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0050] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0051] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0052] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0053] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0054] Example 1

[0055] like Figure 1 As shown in the figure, this embodiment provides an intelligent early warning method for abnormal yaw angle of wind turbines, which includes the following steps:

[0056] Step 1: Preprocess the historical data of the target unit to obtain a matrix, where the first column of the matrix is ​​wind speed, the second column is the wind angle, and the third column is active power.

[0057] Step 2: Divide the active power into compartments using wind speed and wind angle to obtain multiple wind speed-angle compartments;

[0058] Step 3: Construct two-dimensional and three-dimensional matrices based on the obtained multiple wind speed-angle chambers.

[0059] Step 4: Use the obtained two-dimensional matrix to determine whether the dynamic deviation of the wind angle is normal; use the three-dimensional matrix to determine whether the target unit is operating normally. If the unit is operating abnormally, use the three-dimensional matrix to construct a four-dimensional matrix and use the four-dimensional matrix to determine whether the static deviation of the wind angle is normal.

[0060] This embodiment calculates the static and dynamic deviations of the wind angle, enabling a more comprehensive and accurate identification of abnormal yaw angles of the unit.

[0061] Example 2

[0062] Step 1: Read the SCADA data of the unit for three months at the 1-minute level. The SCADA data includes the unit status, wind speed, active power, pitch angle and wind angle. At the same time, obtain the unit cut-in wind speed, rated wind speed and rated power from the unit configuration.

[0063] Step 2: Calculate the amount of SCADA data to be read. If the amount of data is greater than or equal to 116640, perform normal data filtering before full transmission; otherwise, report insufficient raw data and end the calculation.

[0064] Step 3: Filter out data with wind speed greater than the cut-in wind speed but less than the rated wind speed, power greater than 0, unit status as normal power generation, and pitch angle less than 2, and form a three-dimensional matrix Q1 containing wind speed, wind angle and active power.

[0065] Step 4: Calculate the data volume of the three-dimensional matrix Q1. If the data volume is greater than or equal to 51840, perform bin partitioning calculation on the data of the three-dimensional matrix Q1; otherwise, report insufficient useful data and end the calculation.

[0066] Step 5: Calculate the data in the three-dimensional matrix Q1:

[0067] S51, active power is binned into multiple wind speed-angle compartments using wind speed and wind angle, each with a width of 1, resulting in multiple wind speed-angle compartments. The wind speed and wind angle in each wind speed-angle compartment are V, respectively. i and α j ;

[0068] S52, calculate the amount of active power data in each wind speed-angle chamber;

[0069] S53, delete the wind speed-angle chambers with less than 50 data points, obtaining the remaining wind speed-angle chambers, where the wind speed and the wind angle in the remaining wind speed-angle chambers are V, respectively. ii and α jj ;

[0070] S54, calculate the average active power W for each remaining wind speed-angle compartment. ii-jj ;

[0071] S55, the remaining wind speeds - angle chambers corresponding to the wind speed V ii α, the angle of the wind jj and average power W ii-jj Combine them to form a three-dimensional matrix Q2.

[0072] Step 6: Calculate the windward angle α inside the chamber for each wind speed-angle chamber. j The corresponding active power data volume n j The wind angle and active power data are combined to form a two-dimensional matrix, denoted as Q4;

[0073] From matrix Q4, all data n j Obtain the wind angle α corresponding to the maximum amount of data. n ;

[0074] Step 7, if the wind angle α n If the absolute value is greater than or equal to 5, the dynamic deviation of the unit's wind angle is abnormal; otherwise, the dynamic deviation is normal.

[0075] Step 8: Calculate the three-dimensional matrix Q2:

[0076] S81, obtain each wind speed V from the three-dimensional matrix Q2. ii The corresponding maximum average power W MAX ii And obtain the maximum power W. MAX ii Corresponding wind angle α ii ;

[0077] S82, obtain each wind speed V from the three-dimensional matrix Q2. ii The average power W corresponding to the wind angle of 0ii-jj mean W O ii ;

[0078] S83, calculate each wind speed V ii The corresponding maximum power W MAX ii With mean W O ii The power difference W between them ii ;

[0079] S84, calculate all power differences W in the three-dimensional matrix Q2. ii The average power difference W between them μ ;

[0080] S85, calculate each wind speed V ii The sum of the corresponding data size N ii ;

[0081] S86, the obtained wind speed V ii α, the angle of the wind ii The sum of data N ii and power difference W ii Combine them to form a four-dimensional matrix Q3.

[0082] Step 9, if the average power W μ If the power deviation is greater than 5% of the rated power, proceed to step 10 to calculate the static angle deviation; otherwise, if the power deviation is small, the unit is normal.

[0083] Step 10: Calculate the static angle deviation β using the data from the four-dimensional matrix Q3. The calculation formula is as follows:

[0084]

[0085] Where, N ii W is the sum of the data volume. ii Power difference; β ii ii is the static angle deviation corresponding to the i-th row of the four-dimensional matrix; ii is (1, 2, 3, 4, ..., n); n is the row number of the four-dimensional matrix.

[0086] Step 11: If the absolute value of the static angle deviation β is greater than the empirical value (which is 8), then an abnormal static angle deviation is reported; otherwise, the static angle deviation of the unit is normal.

[0087] In calculating the static deviation of the wind angle, this embodiment incorporates the unit's operating mechanism. For example, during the power ramp-up phase, with blade length, air density, and main control program remaining constant, the unit's output power is mainly related to wind speed and wind angle. The use of a multi-parameter automatic weighting method not only effectively calculates the static deviation of the wind angle but also avoids extensive parameter adjustments in the later stages of the calculation, saving significant manpower and time, thereby improving the accuracy of the judgment results for abnormal wind angles.

[0088] Example 3

[0089] This embodiment provides an intelligent early warning system for abnormal yaw angle of a wind turbine, characterized in that it includes:

[0090] The data acquisition unit is used to preprocess the historical data of the acquired target unit to obtain a matrix, wherein the first column of the matrix is ​​wind speed, the second column is the wind angle, and the third column is active power.

[0091] The compartmentalization unit is used to divide active power into compartments based on wind speed and wind angle, resulting in multiple wind speed-angle compartments.

[0092] Matrix construction unit, used to construct two-dimensional and three-dimensional matrices respectively based on the obtained multiple wind speed-angle compartments.

[0093] The anomaly detection unit is used to determine whether the dynamic deviation of the wind angle is normal using the obtained two-dimensional matrix; and to determine whether the target unit is operating normally using the three-dimensional matrix. If the unit is operating abnormally, a four-dimensional matrix is ​​constructed using the three-dimensional matrix, and the four-dimensional matrix is ​​used to determine whether the static deviation of the wind angle is normal.

[0094] This system can more comprehensively and accurately identify problems such as abnormal yaw angle of the unit.

[0095] Example 4

[0096] This embodiment provides a processing device corresponding to the intelligent early warning method for abnormal yaw angle of wind turbine provided in Embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Embodiment 1.

[0097] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the intelligent early warning method for abnormal yaw angle of a wind turbine provided in Embodiment 1.

[0098] In some embodiments, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0099] In other embodiments, the processor can be a general-purpose processor of various types, such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0100] Example 5

[0101] The intelligent early warning method for abnormal yaw angle of a wind turbine in Embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded for executing the intelligent early warning method for abnormal yaw angle of a wind turbine in Embodiment 1.

[0102] A computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device.

[0103] Computer-readable storage media may be, for example, but not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0104] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for intelligent early warning of abnormal yaw angle of a wind turbine, characterized in that, Includes the following steps: Step 1: Preprocess the historical data of the target unit to obtain a matrix, where the first column of the matrix is ​​wind speed, the second column is the wind angle, and the third column is active power. Step 2: Divide the active power into compartments using wind speed and wind angle to obtain multiple wind speed-angle compartments; Step 3: Construct two-dimensional and three-dimensional matrices based on the obtained multiple wind speed-angle chambers; Step 4: Use the obtained two-dimensional matrix to determine whether the dynamic deviation of the wind angle is normal; use the three-dimensional matrix to determine whether the target unit is operating normally. If the target unit is operating abnormally, use the three-dimensional matrix to construct a four-dimensional matrix and use the four-dimensional matrix to determine whether the static deviation of the wind angle is normal. In step 3, a two-dimensional matrix is ​​constructed based on the obtained multiple wind speed-angle chambers. The specific method is as follows: The active power data corresponding to each wind angle in the chamber is statistically analyzed for each wind speed-angle, and the wind angle and active power data are combined to form a two-dimensional matrix; In step 3, a three-dimensional matrix is ​​constructed based on the obtained multiple wind speed-angle chambers. The specific method is as follows: Calculate the amount of active power data in the chamber for each wind speed and angle. Delete the wind speed-angle compartments whose data volume is less than the set value to obtain the remaining wind speed-angle compartments; Calculate the power mean among all active power values ​​corresponding to each remaining wind speed-angle compartment; The average wind speed, wind angle, and power values ​​in all remaining wind speed-angle chambers are combined to form a three-dimensional matrix; In step 3, a four-dimensional matrix is ​​constructed using a three-dimensional matrix. The specific method is as follows: Obtain the average maximum power value corresponding to each wind speed from the three-dimensional matrix; Obtain the windward angle corresponding to the average maximum power; Obtain the average power values ​​for each wind speed when the wind angle is zero from the three-dimensional matrix, and calculate the average value corresponding to all power values. Calculate the power difference between the mean and the maximum power for each maximum power; Obtain the sum of the average power data for each wind speed; The wind speed and wind angle in the three-dimensional matrix, the sum of the calculated data, and the power difference are combined to form a four-dimensional matrix.

2. The intelligent early warning method for abnormal yaw angle of a wind turbine as described in claim 1, characterized in that, In step 1, the historical data of the target unit is preprocessed to obtain a matrix. The specific method is as follows: S11, the historical data includes the target unit status, wind speed, active power, pitch angle and wind angle; obtain the configuration parameters of the target unit, the configuration parameters include cut-in wind speed, rated wind speed and rated power; S12: Select data from historical data that are greater than the cut-in wind speed but less than the rated wind speed, have active power greater than zero, have the target unit in normal power generation status, and have a pitch angle less than 2, and form a matrix.

3. The intelligent early warning method for abnormal yaw angle of a wind turbine as described in claim 1, characterized in that, In step 2, the active power is divided into bins with a width of 1 using wind speed and wind angle to obtain multiple wind speed-angle bins.

4. The intelligent early warning method for abnormal yaw angle of a wind turbine generator according to claim 1, characterized in that, In step 3, the obtained two-dimensional matrix is ​​used to determine whether the dynamic deviation of the wind angle is normal. The specific method is as follows: Obtain the windward angle corresponding to the largest data volume from all data volumes in the two-dimensional matrix; If the absolute value of the windward angle is greater than or equal to the set threshold, the dynamic deviation of the windward angle of the target unit is abnormal; otherwise, the dynamic deviation of the windward angle of the target unit is normal.

5. The intelligent early warning method for abnormal yaw angle of a wind turbine generator according to claim 1, characterized in that, In step 3, a four-dimensional matrix is ​​used to determine whether the static deviation of the wind angle is normal. The specific method is as follows: Calculate the mean power difference for all power differences; The average power difference is compared with a set threshold. If the average power difference is greater than the set threshold, the static deviation of the wind angle is calculated using a four-dimensional matrix; otherwise, the target unit is operating normally. The absolute value of the obtained static deviation of the wind angle is compared with the threshold. If the absolute value of the static deviation of the wind angle is greater than the threshold, the static deviation of the wind angle is abnormal; otherwise, the static deviation of the wind angle is normal.

6. An intelligent early warning system for abnormal yaw angle of a wind turbine generator, characterized in that, include: The data acquisition unit is used to preprocess the historical data of the acquired target unit to obtain a matrix, wherein the first column of the matrix is ​​wind speed, the second column is the wind angle, and the third column is active power. The compartmentalization unit is used to divide active power into compartments based on wind speed and wind angle, resulting in multiple wind speed-angle compartments. Matrix construction unit, used to construct two-dimensional and three-dimensional matrices respectively based on the obtained multiple wind speed-angle compartments. The anomaly detection unit is used to determine whether the dynamic deviation of the wind angle is normal using the obtained two-dimensional matrix; and to determine whether the target unit is operating normally using the three-dimensional matrix. If the target unit is operating abnormally, a four-dimensional matrix is ​​constructed using the three-dimensional matrix, and the four-dimensional matrix is ​​used to determine whether the static deviation of the wind angle is normal. A two-dimensional matrix is ​​constructed based on the obtained multiple wind speed-angle chambers. The specific method is as follows: The active power data corresponding to each wind angle in the chamber is statistically analyzed for each wind speed-angle, and the wind angle and active power data are combined to form a two-dimensional matrix; A three-dimensional matrix is ​​constructed based on the obtained multiple wind speed-angle chambers. The specific method is as follows: Calculate the amount of active power data in the chamber for each wind speed and angle. Delete the wind speed-angle compartments whose data volume is less than the set value to obtain the remaining wind speed-angle compartments; Calculate the power mean among all active power values ​​corresponding to each remaining wind speed-angle compartment; The average wind speed, wind angle, and power values ​​in all remaining wind speed-angle chambers are combined to form a three-dimensional matrix; The specific method for constructing a four-dimensional matrix using a three-dimensional matrix is ​​as follows: Obtain the average maximum power value corresponding to each wind speed from the three-dimensional matrix; Obtain the windward angle corresponding to the average maximum power; Obtain the average power values ​​for each wind speed when the wind angle is zero from the three-dimensional matrix, and calculate the average value corresponding to all power values. Calculate the power difference between the mean and the maximum power for each maximum power; Obtain the sum of the average power data for each wind speed; The wind speed and wind angle in the three-dimensional matrix, the sum of the calculated data, and the power difference are combined to form a four-dimensional matrix.

7. A processing apparatus, the processing apparatus comprising at least a processor and a memory, the memory storing a computer program, characterized in that, When the processor runs the computer program, it performs steps to implement the method of any one of claims 1 to 5.

8. A computer storage medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to perform the steps of the method according to any one of claims 1 to 5.

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