Wind farm data linear interpolation processing method and system

Abstract

The application relates to the technical field of wind farm data processing, and specifically discloses a wind farm data linear interpolation processing method and system, which comprises the following steps: obtaining historical wind speed data of a wind farm, partitioning wind turbine generators in the wind farm according to the historical wind speed data to obtain wind turbine generator partitions; obtaining real-time wind speed data of the wind turbine generators in the wind turbine generator partitions, predicting theoretical power data of the wind turbine generators according to the real-time wind speed data of the wind turbine generators, screening out abnormal data in the theoretical power data of the wind turbine generators and eliminating the abnormal data; filling in the theoretical power data of the wind turbine generators after the abnormal data is eliminated based on a linear interpolation method to obtain complete theoretical power data of the wind turbine generators; determining a target error value according to the real-time wind speed data of the wind turbine generators, and correcting the theoretical power data of the wind turbine generators according to the target error value. The wind farm is partitioned, and the theoretical power data is filled in based on the linear interpolation method, so that the accuracy and completeness of the data are improved.

Description

Technical Field

[0001] This application relates to the field of wind farm data processing technology, and more specifically, to a method and system for linear interpolation processing of wind farm data. Background Technology

[0002] In recent years, the installed capacity of wind power has been increasing year by year across the country, and the reform of the power market has also been deepening year by year. This has put forward higher requirements for effectively evaluating the deviation between the power forecast and the actual real-time output of wind farms during power generation.

[0003] Existing technologies lack specificity for wind farm power prediction and cannot flexibly predict power values ​​based on the specific operating conditions of wind turbines. This results in a large number of abnormal data in the theoretical power data of wind farms. Furthermore, traditional data completion methods have large completion errors and cannot guarantee the completeness and accuracy of the theoretical power data of wind farms. Summary of the Invention

[0004] This invention provides a linear interpolation processing method and system for wind farm data to solve the problems of low integrity and poor accuracy of theoretical power data from wind farms in the prior art, including:

[0005] Obtain historical wind speed data of the wind farm, and divide the wind turbine units in the wind farm into zones based on the historical wind speed data to obtain wind turbine unit zones;

[0006] Obtain real-time wind speed data of wind turbines in the wind turbine group's zone, predict the theoretical power data of wind turbines based on the real-time wind speed data, filter out abnormal data in the theoretical power data of wind turbines, and remove abnormal data.

[0007] The theoretical power data of wind turbines after removing outlier data is filled in using the linear interpolation method to obtain complete theoretical power data of wind turbines.

[0008] The target error value is determined based on the real-time wind speed data of the wind turbine, and the theoretical power data of the wind turbine is corrected based on the target error value.

[0009] Furthermore, the wind turbines within the wind farm are divided into zones based on historical wind speed data, including:

[0010] Based on the historical wind speed data of the wind farm, determine the historical wind speed curves of each wind turbine in the wind farm, obtain the preset wind speed data range, and determine the wind speed frequency distribution of the historical wind speed curves based on the preset wind speed data range.

[0011] Based on the wind speed frequency distribution, a wind speed frequency curve is plotted, a preset wind speed threshold is obtained, and the frequencies corresponding to wind speed data that are greater than the preset wind speed threshold are filtered out from the wind speed frequency curve.

[0012] The wind speed fluctuation coefficient is obtained by calculating the ratio of the frequency corresponding to the wind speed data that exceeds the preset wind speed threshold to the preset allowable frequency.

[0013] Obtain wind speed data that are less than or equal to a preset wind speed threshold from the wind speed frequency curve graph, calculate the average value of the wind speed data that are less than or equal to the preset wind speed threshold from the wind speed frequency curve graph, and multiply the average value of the wind speed data that are less than or equal to the preset wind speed threshold from the wind speed frequency curve graph by the wind speed fluctuation coefficient to obtain the wind speed distribution coefficient.

[0014] Based on the k-means clustering algorithm, wind turbines in the wind farm are clustered according to the wind speed distribution coefficient, and the wind turbine groups are divided according to the clustering results.

[0015] Furthermore, based on the k-means clustering algorithm, wind turbines within the wind farm are clustered according to the wind speed distribution coefficient, including:

[0016] Based on the number of wind turbines in the wind farm, k initial cluster centers are randomly selected, and the Euclidean distance from the wind speed distribution coefficient of each wind turbine to the initial cluster center is calculated.

[0017] Based on the Euclidean distance from the wind speed distribution coefficient to the initial cluster center, each wind turbine is divided into a corresponding partition, and the average value of all wind speed distribution coefficients in each partition is calculated.

[0018] The cluster centers are updated based on the average value of all wind speed distribution coefficients within each cluster. The process is repeated iteratively based on the new cluster centers until the cluster centers no longer change, resulting in k final cluster centers.

[0019] The wind turbines in the wind farm are divided into k wind turbine clusters based on the Euclidean distance from the wind speed distribution coefficient to the final cluster center.

[0020] Furthermore, abnormal data in the real-time power data of wind turbines are filtered out and removed, including:

[0021] Based on the theoretical power data, a theoretical power curve is plotted, and a standard power curve for the corresponding wind turbine unit in the wind turbine unit area is obtained. The sum of the correlation coefficients between the theoretical power curve and the standard power curve within a preset rolling time window is calculated, and time periods where the sum of the correlation coefficients is less than a first preset threshold are selected.

[0022] Calculate the absolute value of the difference between each power data point and the average value of two adjacent power data points during the period when the sum of the correlation coefficients in the theoretical power curve is less than the first preset threshold. Determine the power change rate of the power data point based on the absolute value of the difference between the power data point and the average value of two adjacent power data points.

[0023] If the power change rate of a power data point is greater than the second preset threshold, the power data point will be identified as abnormal data and the abnormal data will be removed.

[0024] Furthermore, the theoretical power data of wind turbines after removing outlier data is filled in using a linear interpolation method, including:

[0025] Obtain the average value of two non-abnormal data points adjacent to the anomalous data point in the theoretical power curve, and determine the initial linear interpolation based on the average value of the non-abnormal data points.

[0026] The interpolation correction coefficient is determined based on the wind turbine group's location, and the initial linear interpolation is corrected based on the interpolation correction coefficient to obtain the linear interpolation.

[0027] Linear interpolation of each abnormal data point is used to supplement the theoretical power data, thus completing the filling of the theoretical power data of the wind turbine.

[0028] Furthermore, interpolation correction coefficients are determined based on the wind turbine cluster zoning location, including:

[0029] Obtain the cluster center values ​​for each wind turbine component. Then, determine the interpolation correction coefficient based on the cluster center values ​​and the correction coefficient calculation formula. The specific formula for calculating the correction coefficient is as follows:

[0030]

[0031] in, These are interpolation correction coefficients. The preset interpolation correction coefficients are used. The value is the cluster center. To preset the allowable wind speed, This is the preset range coefficient.

[0032] Furthermore, the target error value is determined based on the real-time wind speed data of the wind turbine, including:

[0033] A real-time wind speed curve is plotted based on the real-time wind speed data of the wind turbine. The wind speed fluctuation coefficient is calculated based on the real-time wind speed curve. It is then determined whether the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is greater than the third preset threshold.

[0034] If the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is greater than the third preset threshold, then the preset first error value is set as the target error value.

[0035] If the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is less than or equal to the third preset threshold, then determine whether the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is greater than the fourth preset threshold.

[0036] If the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is greater than the fourth preset threshold, then the preset second error value is set as the target error value.

[0037] If the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is less than or equal to the fourth preset threshold, then the preset third error value is set as the target error value.

[0038] Furthermore, the wind speed fluctuation coefficient is calculated based on the real-time wind speed curve, including:

[0039] Obtain a preset sliding time window, and divide the real-time wind speed curve into several sub-wind speed curves according to the preset sliding time window.

[0040] Calculate the absolute value of the slope of each sub-wind speed curve, filter out the sub-wind speed curves whose absolute value of the slope is greater than the fifth preset threshold, calculate the ratio of the number of sub-wind speed curves whose absolute value of the slope is greater than the fifth preset threshold to the preset allowable number, and obtain the wind speed fluctuation coefficient.

[0041] Furthermore, the theoretical power data of the wind turbine is corrected based on the target error value, including:

[0042] Obtain the real-time power curve of the wind turbine, calculate the difference between the real-time power curve and the theoretical power curve, and obtain the theoretical power error;

[0043] Calculate the difference between the theoretical power error and the target error value, and normalize the difference between the theoretical power error and the target error value to obtain the error correction coefficient;

[0044] Multiply the error correction factor by the theoretical power data to obtain the corrected theoretical power data.

[0045] To achieve the above objectives, the present invention also provides a linear interpolation processing system for wind farm data, comprising:

[0046] The data acquisition module is used to acquire historical wind speed data of the wind farm, and divide the wind turbine units in the wind farm into zones based on the historical wind speed data to obtain wind turbine unit zones;

[0047] The power data acquisition module is used to acquire real-time wind speed data of wind turbines in the wind turbine group's zone, predict the theoretical power data of wind turbines based on the real-time wind speed data, filter out abnormal data in the theoretical power data of wind turbines, and remove abnormal data.

[0048] The power data processing module is used to fill in the theoretical power data of the wind turbine after removing abnormal data based on the linear interpolation method, so as to obtain the complete theoretical power data of the wind turbine.

[0049] The power data correction module is used to determine the target error value based on the real-time wind speed data of the wind turbine, and to correct the theoretical power data of the wind turbine based on the target error value.

[0050] The beneficial effects of this invention are as follows:

[0051] By applying the above technical solutions, this invention divides wind turbines into zones based on historical wind speed data of wind farms, sets corresponding correction coefficients for wind turbines in different zones, and fills in the theoretical power data of wind farms based on linear interpolation methods. At the same time, it corrects the theoretical power data by using real-time wind speed data of the turbines, which can effectively prevent deviations in theoretical power data and greatly improve the accuracy and completeness of theoretical power curves. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 The overall flowchart of a linear interpolation processing method for wind farm data proposed in an embodiment of the present invention is shown;

[0054] Figure 2 A schematic diagram of a linear interpolation processing system for wind farm data proposed in an embodiment of the present invention is shown. Detailed Implementation

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] This application provides a method for linear interpolation processing of wind farm data, such as... Figure 1 As shown, it includes:

[0057] S101, Obtain historical wind speed data of the wind farm, divide the wind turbine units in the wind farm into zones based on the historical wind speed data, and obtain wind turbine unit zones;

[0058] In some embodiments of this application, the wind turbines within a wind farm are partitioned based on historical wind speed data, including: determining the historical wind speed curves of each wind turbine within the wind farm based on the historical wind speed data of the wind farm, obtaining a preset wind speed data interval, and determining the wind speed frequency distribution of the historical wind speed curves based on the preset wind speed data intervals; plotting a wind speed frequency curve based on the wind speed frequency distribution, obtaining a preset wind speed threshold, and filtering out the frequencies corresponding to wind speed data in the wind speed frequency curve that are greater than the preset wind speed threshold; calculating the ratio of the frequency corresponding to the wind speed data exceeding the preset wind speed threshold to a preset allowable frequency to obtain a wind speed fluctuation coefficient; obtaining wind speed data in the wind speed frequency curve that are less than or equal to the preset wind speed threshold, calculating the average value of the wind speed data in the wind speed frequency curve that are less than or equal to the preset wind speed threshold, multiplying the average value of the wind speed data in the wind speed frequency curve that are less than or equal to the preset wind speed threshold by the wind speed fluctuation coefficient to obtain a wind speed distribution coefficient; clustering the wind turbines within the wind farm based on the wind speed distribution coefficient using a k-means clustering algorithm, and determining the wind turbine partitions based on the clustering results.

[0059] In this embodiment, multiple preset wind speed data intervals are set, and the historical wind speed curve is divided by the preset wind speed intervals. The frequency of wind speed occurrence in each preset wind speed interval is statistically analyzed by the historical wind speed data of the wind farm, thereby determining the wind speed frequency distribution.

[0060] In some embodiments of this application, the wind turbines in a wind farm are clustered based on the k-means clustering algorithm according to the wind speed distribution coefficient. This includes: randomly selecting k initial cluster centers based on the number of wind turbines in the wind farm; calculating the Euclidean distance from the wind speed distribution coefficient of each wind turbine to the initial cluster center; dividing each wind turbine into corresponding partitions based on the Euclidean distance from the wind speed distribution coefficient to the initial cluster center; calculating the average value of all wind speed distribution coefficients within each partition; updating the cluster centers based on the average value of all wind speed distribution coefficients within each cluster; repeating the iteration based on the new cluster centers until the cluster centers no longer change, resulting in k final cluster centers; and dividing the wind turbines in the wind farm into k wind turbine group partitions based on the Euclidean distance from the wind speed distribution coefficient to the final cluster centers.

[0061] In this embodiment, the number of partitions k is determined by the number of wind turbines in the wind farm. The more wind turbines there are, the larger the corresponding k value. The wind turbines in the wind farm are partitioned based on the k-means clustering algorithm, which facilitates the subsequent adjustment of the power data of each wind turbine.

[0062] S102: Obtain real-time wind speed data of wind turbines in the wind turbine group's zone, predict the theoretical power data of wind turbines based on the real-time wind speed data, filter out abnormal data in the theoretical power data of wind turbines and remove abnormal data.

[0063] In some embodiments of this application, the process of filtering out and removing abnormal data from the real-time power data of wind turbine generators includes: drawing a theoretical power curve based on theoretical power data, obtaining a standard power curve for the corresponding wind turbine generator section, calculating the sum of the correlation coefficients between the theoretical power curve and the standard power curve within a preset rolling time window, and filtering out time periods where the sum of the correlation coefficients is less than a first preset threshold; calculating the absolute value of the difference between each power data point and the average value of two adjacent power data points within the time period where the sum of the correlation coefficients in the theoretical power curve is less than the first preset threshold, determining the power change rate of the power data point based on the absolute value of the difference between the power data point and the average value of two adjacent power data points; and if the power change rate of the power data point is greater than a second preset threshold, then the power data point is identified as abnormal data and the abnormal data is removed.

[0064] In this embodiment, the theoretical power data of the wind turbine can be predicted by establishing a neural network model based on the real-time wind speed data of the wind turbine, or the theoretical power data of the wind turbine can be calculated from the real-time wind speed data of the wind turbine. The optimal power curve of each wind turbine in the historical operation process of each region is used as the standard power curve. By calculating the sum of the correlation coefficients between the theoretical power curve and the standard power curve within a preset rolling time window, the time period with low curve correlation is selected and abnormal data in the time period with low correlation is removed to ensure the accuracy of the theoretical power data.

[0065] S103, fill in the theoretical power data of the wind turbine after removing outlier data based on the linear interpolation method to obtain the complete theoretical power data of the wind turbine.

[0066] In some embodiments of this application, the theoretical power data of wind turbine generators after removing outlier data is filled in using a linear interpolation method, including: obtaining the average value of two non-outlier data points adjacent to the outlier data point in the theoretical power curve; determining an initial linear interpolation value based on the average value of the non-outlier data points; determining an interpolation correction coefficient based on the wind turbine generator group region where the wind turbine generator is located; correcting the initial linear interpolation value based on the interpolation correction coefficient to obtain a linear interpolation value; and supplementing the theoretical power data with the linear interpolation values ​​of each outlier data point to complete the filling in of the theoretical power data of the wind turbine generator.

[0067] In some embodiments of this application, the interpolation correction coefficient is determined based on the wind turbine group's location, including:

[0068] Obtain the cluster center values ​​for each wind turbine component. Then, determine the interpolation correction coefficient based on the cluster center values ​​and the correction coefficient calculation formula. The specific formula for calculating the correction coefficient is as follows:

[0069]

[0070] in, These are interpolation correction coefficients. The preset interpolation correction coefficients are used. The value is the cluster center. To preset the allowable wind speed, Here, exp is the preset range coefficient, and exp is the natural exponential function.

[0071] In this embodiment, the power points that were removed are filled in based on the linear interpolation method, and the interpolation correction coefficient is calculated by the cluster center value. The linear interpolation is corrected by multiplying the interpolation correction coefficient with the linear interpolation, thereby realizing targeted interpolation of the theoretical power data of the wind turbine.

[0072] S104. Determine the target error value based on the real-time wind speed data of the wind turbine, and correct the theoretical power data of the wind turbine based on the target error value.

[0073] In some embodiments of this application, determining the target error value based on the real-time wind speed data of the wind turbine includes: plotting a real-time wind speed curve based on the real-time wind speed data of the wind turbine; calculating the wind speed fluctuation coefficient based on the real-time wind speed curve; determining whether the difference between the wind speed fluctuation coefficient and the preset permissible wind speed fluctuation coefficient is greater than a third preset threshold; if the difference between the wind speed fluctuation coefficient and the preset permissible wind speed fluctuation coefficient is greater than the third preset threshold, then setting a preset first error value as the target error value; if the difference between the wind speed fluctuation coefficient and the preset permissible wind speed fluctuation coefficient is less than or equal to the third preset threshold, then determining whether the difference between the wind speed fluctuation coefficient and the preset permissible wind speed fluctuation coefficient is greater than a fourth preset threshold; if the difference between the wind speed fluctuation coefficient and the preset permissible wind speed fluctuation coefficient is greater than the fourth preset threshold, then setting a preset second error value as the target error value; if the difference between the wind speed fluctuation coefficient and the preset permissible wind speed fluctuation coefficient is less than or equal to the fourth preset threshold, then setting a preset third error value as the target error value.

[0074] In some embodiments of this application, calculating the wind speed fluctuation coefficient based on the real-time wind speed curve includes: obtaining a preset sliding time window; dividing the real-time wind speed curve into several sub-wind speed curves according to the preset sliding time window; calculating the absolute value of the slope of each sub-wind speed curve; filtering out sub-wind speed curves with an absolute value of slope greater than a fifth preset threshold; calculating the ratio of the number of sub-wind speed curves with an absolute value of slope greater than the fifth preset threshold to a preset allowable number; and obtaining the wind speed fluctuation coefficient.

[0075] In this embodiment, the first error value, the second error value, and the third error value increase sequentially. The larger the wind speed fluctuation coefficient, the lower the corresponding target error value. The target error value is dynamically set by the wind speed fluctuation coefficient to increase the accuracy of the theoretical power data.

[0076] In some embodiments of this application, the theoretical power data of the wind turbine is corrected according to the target error value, including: acquiring the real-time power curve of the wind turbine, calculating the difference between the real-time power curve and the theoretical power curve to obtain the theoretical power error; calculating the difference between the theoretical power error and the target error value, normalizing the difference between the theoretical power error and the target error value to obtain the error correction coefficient; and multiplying the error correction coefficient by the theoretical power data to obtain the corrected theoretical power data.

[0077] In this embodiment, the theoretical power error is calculated based on the difference between the real-time power of the wind turbine and the power in the corresponding theoretical power curve. The error correction coefficient is obtained by the deviation between the theoretical power error and the target error value to correct the theoretical power data, thus ensuring the accuracy of the theoretical power data.

[0078] Based on the same technological concept, such as Figure 2 As shown, the present invention also provides a linear interpolation processing system for wind farm data, comprising: a data acquisition module for acquiring historical wind speed data of the wind farm, dividing the wind turbine units within the wind farm into zones based on the historical wind speed data, obtaining wind turbine unit zones; a power data acquisition module for acquiring real-time wind speed data of the wind turbine units within the wind turbine unit zones, predicting the theoretical power data of the wind turbine units based on the real-time wind speed data, filtering out abnormal data in the theoretical power data of the wind turbine units, and removing the abnormal data; a power data processing module for filling in the theoretical power data of the wind turbine units after removing the abnormal data based on a linear interpolation method, obtaining complete theoretical power data of the wind turbine units; and a power data correction module for determining a target error value based on the real-time wind speed data of the wind turbine units, and correcting the theoretical power data of the wind turbine units based on the target error value.

[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0080] By applying the above technical solutions, this invention acquires historical wind speed data of a wind farm, divides the wind turbine units within the wind farm into zones based on this historical wind speed data, obtains wind turbine group zones, acquires real-time wind speed data of the wind turbine units within each zone, predicts the theoretical power data of the wind turbine units based on the real-time wind speed data, filters out and removes abnormal data from the theoretical power data, fills in the theoretical power data of the wind turbine units after removing the abnormal data using a linear interpolation method, and obtains complete theoretical power data of the wind turbine units; determines a target error value based on the real-time wind speed data of the wind turbine units, and corrects the theoretical power data of the wind turbine units based on the target error value. By dividing the wind farm into zones and filling in the theoretical power data using a linear interpolation method, the accuracy and completeness of the data are improved.

[0081] Finally, it should be noted that the above 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.

Claims

1. A wind farm data linear interpolation processing method, characterized in that, The method comprises the following steps: acquiring historical wind speed data of a wind farm, and dividing wind turbines in the wind farm into zones according to the historical wind speed data to obtain wind turbine zoning; acquiring real-time wind speed data of the wind turbines in the wind turbine zoning, predicting theoretical power data of the wind turbines according to the real-time wind speed data of the wind turbines, and screening and removing abnormal data in the theoretical power data of the wind turbines; the method comprises the following steps: drawing a theoretical power curve according to the theoretical power data, acquiring a standard power curve corresponding to the wind turbine zoning of the wind turbines, calculating a sum of correlation coefficients of the theoretical power curve and the standard power curve within a preset rolling time window, and screening a time period in which the sum of the correlation coefficients is less than a first preset threshold; calculating absolute values of differences between each power data point in the time period in which the sum of the correlation coefficients is less than the first preset threshold and average values of adjacent two power data points, and determining a power change rate of the power data point according to the absolute values of the differences; if the power change rate of the power data point is greater than a second preset threshold, the power data point is determined as abnormal data, and the abnormal data is removed; filling the theoretical power data of the wind turbines after removing the abnormal data based on a linear interpolation method to obtain complete theoretical power data of the wind turbines; the method comprises the following steps: acquiring average values of two non-abnormal data points adjacent to the abnormal data point in the theoretical power curve, determining an initial linear interpolation according to the average values of the non-abnormal data points, determining an interpolation correction coefficient according to the wind turbine zoning in which the wind turbines are located, correcting the initial linear interpolation according to the interpolation correction coefficient to obtain a linear interpolation, and supplementing the linear interpolation of each abnormal data point to the theoretical power data to complete the filling of the theoretical power data of the wind turbines; the method of determining the interpolation correction coefficient according to the wind turbine zoning in which the wind turbines are located comprises the following steps: acquiring a clustering center value of the wind turbine zoning, and determining the interpolation correction coefficient according to the clustering center value and a correction coefficient calculation formula; the correction coefficient calculation formula is specifically, ， wherein, is an interpolation correction coefficient, is a preset interpolation correction coefficient, is a cluster center value, is a preset allowable wind speed, is a preset range coefficient; determining a target error value according to the real-time wind speed data of the wind turbines, and correcting the theoretical power data of the wind turbines according to the target error value.

2. The wind farm data line linear interpolation processing method according to claim 1, characterized in that, dividing the wind turbines in the wind farm into zones according to the historical wind speed data of the wind farm comprises the following steps: determining historical wind speed curves of the wind turbines in the wind farm according to the historical wind speed data of the wind farm, acquiring a preset wind speed data interval, and determining a wind speed frequency distribution of the historical wind speed curves according to the preset wind speed data interval; drawing a wind speed frequency curve according to the wind speed frequency distribution, acquiring a preset wind speed threshold, and screening a frequency corresponding to wind speed data greater than the preset wind speed threshold in the wind speed frequency curve; calculating a ratio of the frequency corresponding to the wind speed data greater than the preset wind speed threshold to a preset allowable frequency to obtain a wind speed fluctuation coefficient; acquiring wind speed data less than or equal to the preset wind speed threshold in the wind speed frequency curve, calculating an average value of the wind speed data less than or equal to the preset wind speed threshold in the wind speed frequency curve, and multiplying the average value of the wind speed data less than or equal to the preset wind speed threshold in the wind speed frequency curve by the wind speed fluctuation coefficient to obtain a wind speed distribution coefficient; The wind turbines in the wind farm are clustered according to the wind speed distribution coefficient based on the k-means clustering algorithm, and the wind turbine partition is determined according to the clustering result.

3. The wind farm data line linear interpolation processing method according to claim 2, characterized in that, The wind turbines in the wind farm are clustered according to the wind speed distribution coefficient based on the k-means clustering algorithm, and the wind turbine partition is determined according to the clustering result. Randomly select k initial clustering centers according to the number of wind turbines in the wind farm, calculate the Euclidean distance of the wind speed distribution coefficient of each wind turbine to the initial clustering center; According to the Euclidean distance of the wind speed distribution coefficient to the initial clustering center, each wind turbine is divided into the corresponding partition, and the average value of all wind speed distribution coefficients in each partition is calculated; Update the clustering center according to the average value of all wind speed distribution coefficients in each cluster, and repeat the iteration according to the new clustering center until the clustering center no longer changes, and obtain the final k clustering centers; According to the Euclidean distance of the wind speed distribution coefficient to the final clustering center, the wind turbines in the wind farm are divided into k wind turbine partitions.

4. The wind farm data line linear interpolation processing method according to claim 1, characterized in that, Determine the target error value according to the real-time wind speed data of the wind turbine, including: Draw a real-time wind speed curve according to the real-time wind speed data of the wind turbine, calculate the wind speed fluctuation coefficient according to the real-time wind speed curve, and determine whether the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is greater than the third preset threshold value; If the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is greater than the third preset threshold value, set the preset first error value as the target error value; If the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is less than or equal to the third preset threshold value, determine whether the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is greater than the fourth preset threshold value; If the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is greater than the fourth preset threshold value, set the preset second error value as the target error value; If the difference between the wind speed fluctuation coefficient and the preset allowable wind speed fluctuation coefficient is less than or equal to the fourth preset threshold value, set the preset third error value as the target error value.

5. The wind farm data line linear interpolation processing method according to claim 4, characterized in that, Calculate the wind speed fluctuation coefficient according to the real-time wind speed curve, including: Obtain a preset sliding time window, and divide the real-time wind speed curve into several sub wind speed curves according to the preset sliding time window; Calculate the absolute value of the slope of each sub wind speed curve, select the sub wind speed curve whose absolute value of the slope is greater than the fifth preset threshold value, calculate the ratio of the number of sub wind speed curves whose absolute value of the slope is greater than the fifth preset threshold value to the preset allowable number, and obtain the wind speed fluctuation coefficient.

6. The wind farm data line linear interpolation processing method according to claim 5, characterized in that, According to the target error value, the theoretical power data of the wind turbine is corrected, including: Obtain the real-time power curve of the wind turbine, calculate the difference between the real-time power curve and the theoretical power curve, and obtain the theoretical power error; Calculate the difference between the theoretical power error and the target error value, normalize the difference between the theoretical power error and the target error value, and obtain the error correction coefficient; Multiply the error correction coefficient by the theoretical power data to obtain the corrected theoretical power data.

7. A wind farm data linear interpolation processing system, characterized by, It includes: A data acquisition module is used to acquire historical wind speed data of a wind farm, and the wind turbines in the wind farm are partitioned according to the historical wind speed data to obtain wind turbine partitions. The power data acquisition module is configured to acquire real-time wind speed data of the wind turbine in the partition, predict theoretical power data of the wind turbine according to the real-time wind speed data of the wind turbine, and filter out and eliminate abnormal data in the theoretical power data of the wind turbine; The power data processing module is configured to fill in the theoretical power data of the wind turbine after the abnormal data is eliminated based on a linear interpolation method, and obtain complete theoretical power data of the wind turbine; The power data correction module is configured to determine a target error value according to the real-time wind speed data of the wind turbine, and correct the theoretical power data of the wind turbine according to the target error value.

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