A method and system for detecting power quality of new energy sources in power grid

Abstract

The present invention relates to the field of wind power generation technology, and specifically to a method and system for detecting the quality of power of new energy in a power grid, including: obtaining the power output curve of each wind turbine; obtaining the correlation of the output power of any two wind turbines; obtaining the probability that all the associated wind turbines in each high correlation set are in the same vertical direction of the wind direction; obtaining the parallel direction of the wind direction; obtaining the Euclidean distance between each edge wind turbine and each of the other wind turbines and the angle between the connecting line and the parallel direction of the wind direction; obtaining the average decreasing degree of the correlation of each edge wind turbine; and obtaining the predicted power change function of each wind turbine in the lag time period after the current moment. The present invention predicts the power quality change of wind turbines and compensates for it through the correlation of the power generation quality between multiple wind turbines, thereby ensuring the stability of the power supply of new energy.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method and system for detecting the quality of new energy power in a power grid. Background Art

[0002] In modern society, the widespread application of new energy can reduce dependence on traditional fossil energy, reduce greenhouse gas emissions and air pollution, and help improve environmental quality, protect ecological balance, and mitigate climate change. However, new energy is often greatly affected by environmental factors. When connecting new energy to the power grid, it is necessary to consider whether the quality of new energy meets the standards to ensure the stability of power supply. In the process of quality detection of new energy electricity, the existing technology often analyzes the changes in various electrical parameters of new energy to judge the quality of new energy.

[0003] The existing technology often detects the quality of wind power generation by analyzing the changes in various electrical parameters of new energy. When wind energy fluctuates, in order to ensure the stability of new energy power supply, it is necessary to compensate for wind power generation in a timely manner. However, the existing technology often finds it difficult to predict the fluctuations in the power generation quality of wind turbines, resulting in untimely compensation and unstable power supply. The present invention predicts the changes in the power quality of wind turbines through the correlation between the power generation qualities of multiple wind turbines, so that dynamic voltage restorers can be used for compensation in a timely manner. Summary of the invention

[0004] The present invention provides a method and system for detecting the quality of new energy power in a power grid to solve the existing problems.

[0005] A method and system for detecting the quality of new energy power in a power grid of the present invention adopts the following technical solutions:

[0006] The present invention proposes a method for detecting the quality of power from renewable energy sources in a power grid, the method comprising the following steps:

[0007] Collect the position of each wind turbine and the voltage and current at the output end to obtain the power output curve of each wind turbine;

[0008] Obtain the output power sequence of each wind turbine; obtain the difference matrix of the output power sequences of different wind turbines and perform DTW matching to obtain the correlation of the output powers of any two wind turbines;

[0009] Obtain several associated wind turbines of each wind turbine and construct a high-correlation set; obtain the probability that all associated wind turbines in each high-correlation set are in the same vertical direction of the wind direction according to the position distribution of the associated wind turbines in the high-correlation set, and then obtain the parallel direction of the wind direction;

[0010] Obtain the two outermost edge wind turbines in the direction parallel to the wind direction, and obtain the Euclidean distance between each edge wind turbine and each of the remaining wind turbines and the angle between the connecting line and the direction parallel to the wind direction according to the position coordinates of each wind turbine, and then obtain the average decreasing degree of the correlation of each edge wind turbine, and determine the first windward wind turbine;

[0011] Based on the output power sequence of the first wind turbine facing the wind and other wind turbines, the lag time of each wind turbine relative to the first wind turbine facing the wind is obtained through DTW matching; combined with the power output curve of the first wind turbine facing the wind, the average decreasing degree of the correlation of the first wind turbine facing the wind, the Euclidean distance with each wind turbine, and the angle between the connecting line and the parallel direction of the wind direction, the predicted power change function of each wind turbine in the lag time period after the current moment is obtained, thereby realizing the electric energy compensation of the wind turbine.

[0012] Furthermore, the step of obtaining the output power sequence of each wind turbine generator includes the following specific steps:

[0013] The power output curve of any wind turbine is divided into several segments; the average output power of each segment is calculated to obtain the output power sequence of each wind turbine.

[0014] Furthermore, the correlation between the output powers of any two wind turbines is obtained by obtaining a difference matrix for the output power sequences of different wind turbines and performing DTW matching, and the specific steps include the following:

[0015] Calculate the absolute value of the difference between the average output power of each section in the output power sequence of each wind turbine and the average output power of each section in the output power sequence of other wind turbines to form a difference matrix between each wind turbine and other wind turbines;

[0016] For the correlation of the output power of any two wind turbines, the corresponding specific calculation formula is:

[0017]

[0018] Among them, E i,j represents the correlation between the output power of the i-th wind turbine and the j-th wind turbine; L i,j Represents the DTW distance of the difference matrix between the i-th wind turbine and the j-th wind turbine; norm() normalization function.

[0019] Furthermore, according to the position distribution of the associated wind turbines in the high-association set, the probability that all the associated wind turbines in each high-association set are in the same vertical direction of the wind direction is obtained, and the corresponding specific calculation formula is:

[0020]

[0021] Among them, P i N represents the probability that all associated wind turbines in the high-association set of the i-th wind turbine are in the same vertical direction of the wind; i represents the number of associated wind turbines in the high-association set of the i-th wind turbine; θ i,n represents the angle of the nth associated wind turbine in the high-association set of the i-th wind turbine; norm() represents the normalization function.

[0022] Furthermore, the step of obtaining the parallel direction of the wind direction comprises the following specific steps:

[0023] Among the probabilities that all associated wind turbines in all high-correlation sets are in the same direction perpendicular to the wind direction, the position coordinates of all associated wind turbines in the high-correlation set with the largest probability are fitted with a straight line by the least squares method, and the obtained straight line is used as the perpendicular direction of the wind direction, and then the parallel direction of the wind direction is obtained.

[0024] Furthermore, the specific calculation formula corresponding to the average decreasing degree of the correlation of each marginal wind turbine is obtained as follows:

[0025]

[0026] Where V represents the average decreasing degree of wind turbine correlation; H represents the number of wind turbines; E 1,h represents the correlation between the output power of the first wind turbine facing the wind and the hth wind turbine; l 1,h represents the Euclidean distance between the first wind turbine facing the wind and the hth wind turbine; α 1,h It represents the angle between the line connecting the first wind turbine facing the wind and the h-th wind turbine and the direction parallel to the wind direction; norm() represents the normalization function.

[0027] Furthermore, the step of obtaining the first wind turbine generator facing the wind includes the following specific steps:

[0028] The edge wind turbine generator corresponding to the maximum value of the average decreasing degrees of the correlation between the two edge wind turbine generators is recorded as the first windward wind turbine generator.

[0029] Furthermore, the step of obtaining the lag time of each wind turbine relative to the first wind turbine facing the wind by DTW matching based on the output power sequence of the first wind turbine facing the wind and other wind turbines includes the following specific steps:

[0030] The output power sequence of the first windward generator is recorded as the reference sequence, and the output power sequence of any other wind turbine is recorded as the query sequence;

[0031] The DTW algorithm is used to obtain the time difference between any element of the query sequence and the first element of all corresponding matching elements in the reference sequence;

[0032] The average of all time differences in the query sequence is recorded as the lag time of the wind turbine corresponding to the query sequence relative to the first wind turbine facing the wind.

[0033] Furthermore, the predicted power change function of each wind turbine in the lag time period after the current moment is obtained, and the corresponding specific calculation formula is:

[0034] f′(h,t)=f(1,t)×(1-norm(V max × 1,h ×cosα 1,h ))

[0035] Wherein, f'(h,t) represents the predicted power change function of the hth wind turbine in the lag time period after the current moment; f(1,t) represents the power change function of the output power of the first wind turbine facing the wind in the lag time before the current moment; V max represents the average decreasing degree of relevance of the first wind turbine facing the wind; l 1,h represents the Euclidean distance between the first wind turbine facing the wind and the hth wind turbine; α 1,h It represents the angle between the line connecting the first wind turbine facing the wind and the h-th wind turbine and the direction parallel to the wind direction; norm() represents the normalization function.

[0036] The present invention also proposes a power grid renewable energy power quality detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned power grid renewable energy power quality detection method.

[0037] The beneficial effects of the technical solution of the present invention are:

[0038] By analyzing the output power of the wind turbine array, changes in power quality can be effectively predicted, so that timely compensation can be made to ensure the stability of renewable energy power supply. Specifically, by collecting the position of each wind turbine and the voltage and current at the output end, the power output curve of each wind turbine is obtained, which helps to fully understand the operating status and power output of each wind turbine; obtain the output power sequence of each wind turbine, and obtain the correlation between the output power of any two wind turbines by calculating the output power difference matrix of different wind turbines and performing dynamic time warping (DTW) matching, which is conducive to identifying the power output correlation between wind turbines and finding correlated wind turbines with high correlation; according to the position distribution of wind turbines in the high correlation set, calculate each high correlation The probability that all associated wind turbines in the wind turbine set are in the same vertical direction of the wind direction, and then the parallel direction of the wind direction is determined, which can accurately locate the wind direction distribution of the wind turbine array and provide a basis for subsequent correlation analysis and power quality prediction; obtain the two outermost edge wind turbines in the parallel direction of the wind direction, and calculate the Euclidean distance between the edge wind turbine and the remaining wind turbines and the angle between the connecting line and the parallel direction of the wind direction to obtain the average decreasing degree of correlation, and determine the first wind turbine facing the wind, thereby identifying the first wind turbine facing the wind in the array and the decreasing characteristics of correlation, providing a key reference point for power quality changes. Finally, based on the output power sequence of the first wind turbine facing the wind and other wind turbines, the lag time is obtained through DTW matching, and combined with relevant parameters, the power change function of each wind turbine in the lag time period after the current moment is predicted, so that the power quality changes of the rear wind turbines can be accurately predicted, power compensation can be carried out in advance, and the stability and reliability of power supply can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 The present invention is a flowchart of the steps of a method for detecting the quality of power from renewable energy sources in a power grid. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method and system for detecting the quality of power of new energy sources in a power grid proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0043] The specific scheme of a method and system for detecting power quality of renewable energy in a power grid provided by the present invention is described in detail below with reference to the accompanying drawings.

[0044] See also Figure 1 , which shows a flow chart of a method for detecting the quality of new energy power in a power grid provided by an embodiment of the present invention, the method comprising the following steps:

[0045] Step S001: Collect the position of each wind turbine and the voltage and current at the output end to obtain the power output curve of each wind turbine.

[0046] The purpose of this embodiment is to predict the power change curve of the wind turbine in the future time period to ensure the stability of the new energy power supply. Therefore, it is necessary to obtain the voltage and current at the output end of each wind turbine in real time.

[0047] Specifically, the two-dimensional position coordinates of each wind turbine are obtained from the background management system, and the voltage and current at the output end of each wind turbine are collected in real time to calculate the real-time power data of each wind turbine. The collection time in this embodiment is set to T = 1 hour, and the collection interval is set to 1s. The real-time power data of each wind turbine collected is used as the ordinate and the collection time is used as the abscissa to construct a plane rectangular coordinate system. The collection time and the real-time power data of each wind turbine are projected into the plane rectangular coordinate system to obtain the power output curve of each wind turbine.

[0048] Step S002: Obtain the output power sequence of each wind turbine; obtain the difference matrix of the output power sequences of different wind turbines and perform DTW matching to obtain the correlation between the output powers of any two wind turbines.

[0049] It should be noted that the power generation quality of wind turbines is affected by many factors, among which the change of wind speed and wind direction is one of the most important factors. When the wind speed changes, the blade speed of the wind turbine changes accordingly, resulting in large fluctuations in power generation and output power, thus affecting the power quality. Similarly, the high frequency of wind direction changes will cause the turbine to yaw frequently, further making the output power unstable. In the wind turbine array, although the wind speed and wind direction received by each wind turbine are similar, the output power change of the generator at the downwind is affected by the wind direction first, resulting in a certain lag in the output power change of the generator at the downwind. In order to evaluate the correlation of power generation data between different generators, the correlation of their output powers can be calculated. The higher the correlation, the more likely the power generation data changes of the two generators are similar, and the predicted power quality changes also have a higher reference value; in order to compare the output power curves of any two generators, the dynamic time warping (DTW) method can be used to match two time series through the DTW method and calculate the corresponding DTW distance. The smaller the distance, the more similar the output power curves of the two generators are, and the greater their correlation.

[0050] Specifically, the power output curve of each wind turbine is divided into several segments. This embodiment is described by taking the preset division time of 1 minute and the number of division segments of 60 as an example; the average output power of each small segment is calculated to obtain the output power sequence of each wind turbine, which is expressed as follows:

[0051] Q i =(q i,1 ,q i,2 ,…,q i,60 )

[0052] Among them, Q i represents the output power sequence of the i-th wind turbine; q i,1 represents the average output power of the first segment in the output power sequence of the i-th wind turbine; q i,60 It represents the average output power of the 60th segment in the output power sequence of the i-th wind turbine.

[0053] Furthermore, the absolute value of the difference between the average output power of each segment in the output power sequence of each wind turbine and the average output power of each segment in the output power sequence of other wind turbines is calculated to form a difference matrix between each wind turbine and the other two wind turbines, which is specifically expressed as follows:

[0054]

[0055] Among them, C i,j represents the difference matrix between the i-th wind turbine and the j-th wind turbine; qi,1 represents the average output power of the first segment in the output power sequence of the i-th wind turbine; q j,1 represents the average output power of the first segment in the output power sequence of the jth wind turbine; q i,60 represents the average output power of the 60th segment in the output power sequence of the i-th wind turbine generator; q j,60 represents the average output power of the 60th segment in the output power sequence of the j-th wind turbine; | | represents the absolute value function.

[0056] Furthermore, for each difference matrix, the DTW distance of each difference matrix is ​​obtained by the DTW dynamic programming algorithm. It should be noted that the DTW dynamic programming algorithm is a common and well-known algorithm, and the specific method is not described here. Then, the correlation of the output power of any two wind turbines can be obtained, and the specific calculation formula is:

[0057]

[0058] Among them, E i,j represents the correlation between the output power of the i-th wind turbine and the j-th wind turbine; L i,j represents the DTW distance of the difference matrix between the i-th wind turbine and the j-th wind turbine; norm() is a linear normalization function, and the normalization object is the DTW distance of the difference matrix between any two wind turbines.

[0059] It should be noted that the smaller the DTW distance of the difference matrix of the wind turbines, the greater the correlation E between the output power of the i-th wind turbine and the j-th wind turbine. i,j The stronger.

[0060] Step S003: obtain several associated wind turbines of each wind turbine and construct a high-correlation set; according to the position distribution of the associated wind turbines in the high-correlation set, obtain the probability that all the associated wind turbines in each high-correlation set are in the same vertical direction of the wind direction, and then obtain the parallel direction of the wind direction.

[0061] It should be noted that along the direction of wind blowing, when the wind blows from the upwind outlet to the downwind outlet, the wind force may decrease, and the wind direction reaching the downwind outlet may also have some slight changes. Therefore, the output power of wind turbines in the direction perpendicular to the wind direction has a greater correlation, while the correlation of wind turbines parallel to the wind direction may decrease, and the smaller the distance between wind turbines, the greater the correlation.

[0062] Specifically, the preset correlation threshold of this embodiment is 0.9, which is used as an example for description. Other implementation modes may be set to other values, which are not limited in this embodiment; a number of wind turbines whose correlation with the output power of any wind turbine is greater than the correlation threshold are recorded as the associated wind turbines of the wind turbine and a high correlation set is constructed; in the high correlation set of the wind turbine, the angle between the connecting line of any associated wind turbine and its two nearest adjacent associated wind turbines is calculated, and recorded as the angle of the associated wind turbine in the high correlation set; according to all the angles in the high correlation set, the probability that all the associated wind turbines in the high correlation set are in the same vertical direction of the wind direction is obtained, and the specific calculation formula is:

[0063]

[0064] Among them, P i represents the probability that all associated wind turbines in the high-association set of the i-th wind turbine are in the same vertical direction of the wind; Ni represents the number of associated wind turbines in the high-association set of the i-th wind turbine; θ i,n represents the angle of the nth associated wind turbine in the highly associated set of the i-th wind turbine; norm() represents the linear normalization function, and the normalized object is the angle of all wind turbines.

[0065] It should be noted that It represents the reciprocal of the average angle of all associated wind turbines in the high-correlation set of the i-th wind turbine. The larger the average angle, the smaller the probability that these associated wind turbines are in the same straight line.

[0066] Furthermore, the probability that all associated wind turbines in each high correlation set are in the same vertical direction of the wind direction is obtained; the position coordinates of all associated wind turbines in the high correlation set with the largest probability are fitted with a straight line by the least squares method, and the obtained straight line is used as the vertical direction of the wind direction, and then the parallel direction of the wind direction is obtained; it should be noted that the vertical direction and the parallel direction of the wind direction refer to the corresponding straight lines, and do not include the specific direction.

[0067] Step S004: Obtain the two outermost edge wind turbines in the direction parallel to the wind direction, and obtain the Euclidean distance between each edge wind turbine and each of the other wind turbines and the angle between the connecting line and the direction parallel to the wind direction according to the position coordinates of each wind turbine, and then obtain the average decreasing degree of correlation of each edge wind turbine, and determine the first wind turbine facing the wind.

[0068] It should be noted that, in order to calculate the average decreasing degree of the correlation of wind turbines, it is necessary to determine the first wind turbine facing the wind. The first wind turbine facing the wind can be determined based on the correlation of the wind turbine output power, the physical coordinates of the wind turbine and the parallel direction of the wind direction.

[0069] Specifically, two edge wind turbines at the outermost ends in the direction parallel to the wind direction are obtained, and any one of the edge wind turbines is taken as the first wind turbine facing the wind. According to its position coordinates with other wind turbines, its Euclidean distance with other wind turbines and the angle between the line connecting it with other wind turbines and the direction parallel to the wind direction are obtained, and then the average decreasing degree of the correlation of wind turbines is obtained. The specific calculation formula is:

[0070]

[0071] Where V represents the average decreasing degree of wind turbine correlation; H represents the number of wind turbines; E 1,h represents the correlation between the output power of the first wind turbine facing the wind and the hth wind turbine; l 1,h represents the Euclidean distance between the first wind turbine facing the wind and the hth wind turbine; α 1,h represents the angle between the line connecting the first wind turbine facing the wind and the hth wind turbine and the parallel direction of the wind direction; norm() represents the linear normalization function, and the normalization object is the angle between the two edge wind turbines.

[0072] It should be noted that l 1,h ×cosα 1,h It represents the projection value of the line connecting the first wind turbine facing the wind and the h-th wind turbine in the direction parallel to the wind direction; It represents the rate of change of the correlation of the output power of two wind turbines in the direction parallel to the wind direction. The greater the average rate of change, the greater the average decrease in the correlation of these generators.

[0073] Furthermore, when another edge wind turbine is the first wind turbine facing the wind, the average decreasing degree of the wind turbine correlation is obtained in the same way; the edge wind turbine corresponding to the maximum value of the average decreasing degrees of the correlation of the two edge wind turbines is recorded as the first wind turbine facing the wind.

[0074] Step S005: Based on the output power sequence of the first wind turbine facing the wind and other wind turbines, the lag time of each wind turbine relative to the first wind turbine facing the wind is obtained through DTW matching; combined with the power output curve of the first wind turbine facing the wind, the average decreasing degree of the correlation of the first wind turbine facing the wind, the Euclidean distance with each wind turbine, and the angle between the connecting line and the parallel direction of the wind direction, the predicted power change function of each wind turbine in the lag time period after the current moment is obtained, thereby realizing the electric energy compensation of the wind turbine.

[0075] It should be noted that for the prediction of the power generation of a certain generator in the future, since the power generation function of the rear-row generators has a certain lag with respect to the first windward generator, it is necessary to calculate the lag time between the current generator and the first windward generator; and because of the attenuation of wind force and slight changes in wind direction, the power generation function of the rear-row generators has a certain attenuation characteristic with respect to the first windward generator, which can be estimated by the average degree of decrease of the generators.

[0076] Specifically, the output power sequence of the first windward generator is recorded as the reference sequence, and the output power sequence of any other wind turbine is recorded as the query sequence; the time difference between any element of the query sequence and the first element of all corresponding matching elements in the reference sequence is obtained by the DTW algorithm (each element in the output power sequence corresponds to a period of acquisition time, and the first acquisition time in a period of acquisition time is used as the acquisition time corresponding to the element), and the average of all time differences in the query sequence is recorded as the lag time of the wind turbine corresponding to the query sequence relative to the first windward wind turbine; the power change function of the output power of the first windward generator within the length of the lag time before the current moment is obtained; according to the average decreasing degree of the correlation of the first windward generator, the Euclidean distance between the first windward wind turbine and each wind turbine, and the angle between the connecting line of the first windward wind turbine and each wind turbine and the parallel direction of the wind direction, the predicted power change function of each wind turbine in the lag time period after the current moment is obtained, and the specific calculation formula is:

[0077] f′(h,t)=f(1,t)×(1-norm(V max × 1,h ×cosα 1,h ))

[0078] Wherein, f'(h,t) represents the predicted power change function of the hth wind turbine in the lag time period after the current moment; f(1,t) represents the power change function of the output power of the first wind turbine facing the wind in the lag time before the current moment (obtained based on the power output curve of the first wind turbine facing the wind); Vmax represents the average decreasing degree of relevance of the first wind turbine facing the wind; l 1,h represents the Euclidean distance between the first wind turbine facing the wind and the hth wind turbine; α 1,h represents the angle between the line connecting the first wind turbine and the hth wind turbine and the direction parallel to the wind direction; norm() represents the linear normalization function, and the normalization object is the V max × 1,h ×cosα 1,h .

[0079] It should be noted that l 1,h ×cosα 1,h V represents the projection value of the line connecting the first wind turbine and the hth wind turbine in the direction parallel to the wind direction. max × 1,h ×cosα 1,h It represents the product of the average decreasing degree of the correlation between the first wind turbine facing the wind and the h-th wind turbine and the projection value of the h-th wind turbine in the direction parallel to the wind direction. The larger the projection value, the greater the attenuation degree and the smaller the predicted function value.

[0080] Furthermore, based on the predicted power change function of each wind turbine in the lag time period after the current moment, the predicted output power value of each wind turbine after the current moment is obtained, and then the wind turbine is compensated through the dynamic voltage restorer to realize the detection and regulation of the power quality of the power grid. Dynamic voltage transformation compensation based on the output power prediction value is not the focus of this embodiment, and the existing method can be used, which will not be repeated in this embodiment.

[0081] At this point, this embodiment is completed.

[0082] The present invention also provides a power grid renewable energy power quality detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned power grid renewable energy power quality detection method.

[0083] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting the quality of new energy power in a power grid, characterized in that: The method comprises the following steps: Collect the position of each wind turbine and the voltage and current at the output end to obtain the power output curve of each wind turbine; Obtain the output power sequence of each wind turbine; obtain the difference matrix of the output power sequences of different wind turbines and perform DTW matching to obtain the correlation of the output powers of any two wind turbines; Obtain several associated wind turbines of each wind turbine and construct a high-correlation set; obtain the probability that all associated wind turbines in each high-correlation set are in the same vertical direction of the wind direction according to the position distribution of the associated wind turbines in the high-correlation set, and then obtain the parallel direction of the wind direction; Obtain the two outermost edge wind turbines in the direction parallel to the wind direction, and obtain the Euclidean distance between each edge wind turbine and each of the remaining wind turbines and the angle between the connecting line and the direction parallel to the wind direction according to the position coordinates of each wind turbine, and then obtain the average decreasing degree of the correlation of each edge wind turbine, and determine the first windward wind turbine; Based on the output power sequence of the first wind turbine facing the wind and other wind turbines, the lag time of each wind turbine relative to the first wind turbine facing the wind is obtained through DTW matching; combined with the power output curve of the first wind turbine facing the wind, the average decreasing degree of the correlation of the first wind turbine facing the wind, the Euclidean distance with each wind turbine, and the angle between the connecting line and the parallel direction of the wind direction, the predicted power change function of each wind turbine in the lag time period after the current moment is obtained, thereby realizing the electric energy compensation of the wind turbine.

2. A method for detecting the quality of new energy power in a power grid according to claim 1, characterized in that: The specific steps of obtaining the output power sequence of each wind turbine generator are as follows: The power output curve of any wind turbine is divided into several segments; the average output power of each segment is calculated to obtain the output power sequence of each wind turbine.

3. According to claim 1, a method for detecting the quality of new energy power in a power grid is characterized in that: The method of obtaining the difference matrix of the output power sequences of different wind turbines and performing DTW matching to obtain the correlation of the output powers of any two wind turbines includes the following specific steps: Calculate the absolute value of the difference between the average output power of each section in the output power sequence of each wind turbine and the average output power of each section in the output power sequence of other wind turbines to form a difference matrix between each wind turbine and other wind turbines; For the correlation of the output power of any two wind turbines, the corresponding specific calculation formula is: Among them, E i,j represents the correlation between the output power of the i-th wind turbine and the j-th wind turbine; L i,j represents the DTW distance of the difference matrix between the i-th wind turbine and the j-th wind turbine; norm() is the normalization function.

4. A method for detecting the quality of new energy power in a power grid according to claim 1, characterized in that: According to the position distribution of the associated wind turbines in the high-association set, the probability that all the associated wind turbines in each high-association set are in the same vertical direction of the wind direction is obtained, and the corresponding specific calculation formula is: Among them, P i N represents the probability that all associated wind turbines in the high-association set of the i-th wind turbine are in the same vertical direction of the wind; i represents the number of associated wind turbines in the high-association set of the i-th wind turbine; θ i,n represents the angle of the nth associated wind turbine in the high-correlation set of the i-th wind turbine; norm() represents the normalization function.

5. A method for detecting the quality of new energy power in a power grid according to claim 1, characterized in that: The step of obtaining the parallel direction of the wind direction comprises the following specific steps: Among the probabilities that all associated wind turbines in all high-correlation sets are in the same direction perpendicular to the wind direction, the position coordinates of all associated wind turbines in the high-correlation set with the largest probability are fitted with a straight line by the least squares method, and the obtained straight line is used as the perpendicular direction of the wind direction, and then the parallel direction of the wind direction is obtained.

6. A method for detecting the quality of new energy power in a power grid according to claim 1, characterized in that: The specific calculation formula corresponding to the average decreasing degree of correlation of each marginal wind turbine is as follows: Where V represents the average decreasing degree of wind turbine correlation; H represents the number of wind turbines; E 1,h represents the correlation between the output power of the first wind turbine facing the wind and the hth wind turbine; l 1,h represents the Euclidean distance between the first wind turbine facing the wind and the hth wind turbine; α 1,h represents the angle between the line connecting the first wind turbine facing the wind and the h-th wind turbine and the direction parallel to the wind direction; norm( ) represents the normalization function.

7. A method for detecting the quality of new energy power in a power grid according to claim 1, characterized in that: The step of obtaining the first windward wind turbine comprises the following specific steps: The edge wind turbine generator corresponding to the maximum value of the average decreasing degrees of the correlation between the two edge wind turbine generators is recorded as the first windward wind turbine generator.

8. A method for detecting the quality of new energy power in a power grid according to claim 1, characterized in that: The method of obtaining the lag time of each wind turbine relative to the first wind turbine facing the wind by DTW matching based on the output power sequence of the first wind turbine facing the wind and other wind turbines includes the following specific steps: The output power sequence of the first windward generator is recorded as the reference sequence, and the output power sequence of any remaining wind turbines is recorded as the query sequence; The DTW algorithm is used to obtain the time difference between any element of the query sequence and the first element of all corresponding matching elements in the reference sequence; The average of all time differences in the query sequence is recorded as the lag time of the wind turbine corresponding to the query sequence relative to the first wind turbine facing the wind.

9. A method for detecting the quality of new energy power in a power grid according to claim 1, characterized in that: The predicted power change function of each wind turbine in the lag time period after the current moment is obtained, and the corresponding specific calculation formula is: f'(h,t)=f(1,t)×(1-norm(V max ×l 1,h ×cosα 1,h )) Wherein, f'(h,t) represents the predicted power change function of the hth wind turbine in the lag time period after the current moment; f(1,t) represents the power change function of the output power of the first wind turbine facing the wind in the lag time before the current moment; V max represents the average decreasing degree of relevance of the first wind turbine facing the wind; l 1,h represents the Euclidean distance between the first wind turbine facing the wind and the hth wind turbine; α 1,h represents the angle between the line connecting the first wind turbine facing the wind and the h-th wind turbine and the direction parallel to the wind direction; norm( ) represents the normalization function.

10. A power grid new energy power quality detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the computer program is executed by the processor, the steps of a method for detecting the quality of new energy power in a power grid as described in any one of claims 1 to 9 are implemented.

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