Wind turbine generator performance improvement method and system based on data analysis

Abstract

The invention discloses a wind turbine generator performance improvement method and system based on data analysis, and belongs to the technical field of the wind energy industry, and the method comprises the following steps: S1, obtaining and analyzing the operation data of each wind turbine generator, and obtaining the operation evaluation information of each wind turbine generator; s2, judging according to the operation evaluation information of each wind turbine generator, if the operation evaluation information of a certain wind turbine generator is normal operation, recording the wind turbine generator as a normal wind turbine generator and executing S3, and if the operation evaluation information of the certain wind turbine generator is abnormal operation, recording the wind turbine generator as an abnormal wind turbine generator and executing S4; s3, setting a verification time period, acquiring operation constraint parameters of the wind turbine generator in the verification time period, analyzing the operation constraint parameters to obtain an execution adjustment parameter set of each normal-state wind turbine generator, and performing performance adjustment; and S4, carrying out inspection analysis on the abnormal wind turbine generator, and carrying out display reminding through the management terminal. The problems that in the prior art, evaluation parameters are single, and an adjusting mode lacks pertinence are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind energy industry, and in particular to a method and system for improving the performance of a wind turbine generator set based on data analysis. Background Art

[0002] With the growth of global energy demand and the enhancement of environmental awareness, wind energy has attracted much attention. However, wind turbines are often affected by many factors and their performance fluctuates greatly. The existing wind turbine performance improvement method system is achieved by measuring and controlling the lift of the blades to adjust the flap angle or evaluating the yaw performance and energy conversion efficiency of the wind turbine.

[0003] For example, the invention patent with announcement number: CN109162878B announces a wind turbine intelligent blade measurement and control method and system, including: comparing the read real-time lift value y(k) of the blade with the target lift value r(k) to obtain a deviation e(k)=r(k)-y(k), designing a single neuron incremental PID controller, calculating the increment Δθr(k) of the flap angle control amount, and obtaining the angle target value θr(k) of the trailing edge flap on the blade through z transformation; calculating the difference eθ(k) between the flap target deflection angle and the flap real-time deflection angle through a PI controller to obtain a control amount Δu(k), and obtaining the motor shaft target angle value u(k) of the servo motor controlling the trailing edge flap through z transformation.

[0004] For example, the invention patent with announcement number: CN110552849B announces a method and device for evaluating the yaw performance and energy conversion efficiency of a wind turbine, including: obtaining the wind speed, the rotation speed of the generator and the electromagnetic torque of the generator; calculating the wind energy absorbed by the wind rotor according to the rotation speed of the generator, the electromagnetic torque of the generator and other quantities; calculating the wind energy conversion efficiency of converting wind energy into mechanical energy according to the wind energy absorbed by the wind rotor, the air density of the wind farm, the wind speed and the plane area of ​​the wind rotor; obtaining the yaw quality coefficient according to the wind energy conversion efficiency and the wind energy utilization coefficient; and evaluating the yaw performance of the wind turbine according to the yaw quality coefficient.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] In the existing technology, control mainly relies on fixed sensor data and preset performance adjustment methods. However, due to the complexity and variability of the wind energy environment, these methods are often difficult to fully and accurately reflect the actual operating status of wind turbines. Therefore, there are problems such as single evaluation parameters and lack of targeted adjustment methods. Summary of the invention

[0007] The embodiments of the present application provide a method and system for improving the performance of a wind turbine generator set based on data analysis, thereby solving the problems of single evaluation parameters and lack of targeted adjustment methods in the prior art, and achieving accurate evaluation and targeted optimization of the operating status of the wind turbine generator set.

[0008] The embodiment of the present application provides a method for improving the performance of a wind turbine generator set based on data analysis, comprising the following steps: S1, obtaining and analyzing the operation data of each wind turbine generator set to obtain the operation evaluation index of each wind turbine generator set, and obtaining the operation evaluation information of each wind turbine generator set according to the operation evaluation index of each wind turbine generator set; S2, judging according to the operation evaluation information of each wind turbine generator set, if the operation evaluation information of a certain wind turbine generator set is normal operation, then the wind turbine generator set is recorded as a normal wind turbine generator set, and S3 is executed; if the operation evaluation information of a certain wind turbine generator set is abnormal operation, then the wind turbine generator set is recorded as an abnormal wind turbine generator set, and S4 is executed; S3, set a verification period, obtain the wind turbine operating constraint parameters during the verification period for analysis, obtain the wind turbine pre-adjustment parameter set, and count the electrical performance parameters of each normal wind turbine to correct the wind turbine pre-adjustment parameter set to obtain the execution adjustment parameter set of each normal wind turbine, and adjust the performance of each normal wind turbine according to the execution adjustment parameter set of each normal wind turbine; S4, transmit the inspection verification signal to the equipment quality inspection control center through the central controller, and conduct inspection analysis on abnormal wind turbines, build a performance screening order for abnormal units, and display reminders through the management terminal.

[0009] Furthermore, the operation data of each wind turbine is obtained and analyzed to obtain the operation evaluation index of each wind turbine, specifically including: obtaining the operation data of each wind turbine, including the generator output power, electric energy production speed, instantaneous energy efficiency ratio and power generation frequency of each wind turbine; obtaining the operation data rated set preset in the database, including the generator output power rated value, electric energy production speed rated value, instantaneous energy efficiency ratio rated value and power generation frequency rated value; analyzing the operation data of each wind turbine with the operation data rated set to obtain the operation evaluation index of each wind turbine; the operation evaluation index of each wind turbine is used to characterize the operation effect of the wind turbine.

[0010] Furthermore, the operation evaluation information of each wind turbine group is obtained according to the operation evaluation index of each wind turbine group. The specific method is: obtain the wind turbine operation evaluation threshold preset in the database, and match it with the operation evaluation index of each wind turbine group. If the operation evaluation index of a wind turbine group is above the wind turbine operation evaluation threshold, then the operation evaluation information of the wind turbine group is normal operation. If the operation evaluation index of a wind turbine group is less than the wind turbine operation evaluation threshold, then the operation evaluation information of the wind turbine group is abnormal operation.

[0011] Furthermore, the wind turbine operating constraint parameters during the verification period are obtained and analyzed to obtain a pre-adjustment parameter set of the wind turbine, and the specific method is: obtaining the wind turbine operating constraint parameters during the verification period, including grid demand parameters and external influence parameters; the grid demand parameters include the frequency, maximum voltage, average power factor, standby capacity call rate and dispatch load demand change rate measured by the grid; the external influence parameters include the average wind speed of the environment, the average humidity of the environment, the average temperature of the environment and the average concentration of dust; obtaining the grid demand rated set preset in the database, including the frequency rated value, voltage rated value, power factor rated value, standby capacity call rate rated value and dispatch load demand change rate rated value measured by the grid; obtaining the external influence parameter reference set preset in the database, including the wind speed reference value, the ambient humidity reference value, the ambient temperature reference value and the dust concentration reference value; analyzing the grid demand parameters and the grid demand rated set to obtain the grid demand index, analyzing the external influence parameters and the external influence parameter reference set to obtain the external influence index, and obtaining the wind turbine operation verification index based on the grid demand index and the external influence index; the wind turbine operation verification index is used to characterize the expected degree of the wind turbine.

[0012] Furthermore, the wind turbine pre-adjustment parameter set comprises the following specific steps: obtaining the operation verification index intervals of each wind turbine set preset in the database and the ideal parameter set of the wind turbine set corresponding to each wind turbine set operation verification index interval; if the wind turbine set operation verification index is within a preset wind turbine set operation verification index interval, then obtaining the ideal parameter set of the wind turbine set corresponding to the wind turbine set operation verification index interval as the wind turbine pre-adjustment parameter set; the wind turbine pre-adjustment parameter set comprises: the pre-adjusted blade angle and the pre-adjusted engine speed of the wind turbine set.

[0013] Furthermore, the statistical electrical performance parameters of each normal wind turbine set correct the pre-adjustment parameter set of the wind turbine set to obtain the execution adjustment parameter set of each normal wind turbine set, specifically including: obtaining the electrical performance parameters of each normal wind turbine set, including the average output power of the generator, the average input current fluctuation of the converter, the average input voltage fluctuation of the converter and the average reactive power of each normal wind turbine set during the verification period; obtaining the electrical performance verification parameter set preset in the database, including the rated value of the generator output power, the allowable value of the input current fluctuation of the converter, the allowable value of the voltage fluctuation rate and the rated value of the reactive power; analyzing the electrical performance parameters of each normal wind turbine set with the electrical performance rated parameter set to obtain the performance evaluation index of each normal wind turbine set; analyzing according to the performance evaluation index of each normal wind turbine set to obtain the execution adjustment parameter set of each normal wind turbine set; the performance evaluation index of each normal wind turbine set is used to characterize the actual operating performance of each normal wind turbine set.

[0014] Furthermore, the performance evaluation index of each normal wind turbine generator set is obtained by:

[0015]

[0016] It should be noted that YC j represents the performance evaluation index of the jth normal wind turbine, j represents the number of the normal wind turbine, j = 1, 2, ..., J, J represents the total number of normal wind turbines, CW j represents the average output power of the generator of the jth normal wind turbine, ΔCW1 represents the rated output power of the generator, and RI j represents the average input current fluctuation of the jth wind turbine in normal condition, ΔRI1 represents the allowable value of the input current fluctuation of the converter, RV j represents the average input voltage fluctuation of the converter of the jth normal wind turbine, ΔRV represents the allowable value of the converter input voltage fluctuation rate, WL j represents the average reactive power of the jth normal wind turbine, ΔWL represents the reactive power rating, ε1 represents the influence weight of the average output power of the generator, ε2 represents the influence weight of the current fluctuation, ε3 represents the influence weight of the voltage fluctuation rate, and ε4 represents the influence weight of the reactive power.

[0017] Furthermore, the analysis is performed based on the performance evaluation indicators of each normal wind turbine set to obtain the execution adjustment parameter set of each normal wind turbine set, specifically including: obtaining each performance evaluation indicator interval preset in the database and the operation correction parameter set corresponding to each performance evaluation indicator interval, if the performance evaluation indicator of a normal wind turbine set is within a certain performance evaluation indicator interval, then obtaining the operation correction parameter set corresponding to the performance evaluation indicator interval as the operation correction parameter set of the normal wind turbine set; the execution adjustment parameter set of the normal wind turbine set includes the blade angle correction value and the engine speed correction value of each normal wind turbine set; according to the pre-adjustment parameter set of the wind turbine set, and merging them one by one with the operation correction parameter set of the normal wind turbine set, to obtain the execution adjustment parameter set of each normal wind turbine set, the execution adjustment parameter set includes the target execution control blade angle and the target execution control engine speed.

[0018] Furthermore, the abnormal wind turbines are inspected and analyzed, a performance screening order of the abnormal turbines is constructed, and a management terminal is used for displaying reminders. The specific method is: using a drone to collect multi-dimensional images of each abnormal wind turbine, and extracting a collection of appearance defect data of each abnormal wind turbine, the collection of appearance defect data includes the maximum blade depression depth, the maximum blade depression area, the number of blade cracks and the maximum blade crack length of each abnormal wind turbine; based on the collection of appearance defect data of each abnormal wind turbine, analysis is performed to obtain an appearance defect index of each abnormal wind turbine; based on the operation evaluation index of the abnormal wind turbine, the abnormal wind turbine operation evaluation index corresponding to each abnormal wind turbine is extracted, and combined with the appearance defect index of the abnormal wind turbine, the inspection priority index of the abnormal wind turbine is obtained; according to the inspection priority index of the abnormal wind turbine, it is arranged in order from large to small, to obtain a performance screening order of the abnormal turbine and display reminders through the management terminal.

[0019] A wind turbine performance improvement system based on data analysis, characterized in that it comprises: a wind turbine operation evaluation module, an execution judgment module, an adjustment parameter matching module and a performance analysis module; wherein the wind turbine operation evaluation module is used to obtain and analyze the operation data of each wind turbine, obtain the operation evaluation index of each wind turbine, and obtain the operation evaluation information of each wind turbine according to the operation evaluation index of each wind turbine; the execution judgment module is used to judge according to the operation evaluation information of each wind turbine, if the operation evaluation information of a certain wind turbine is normal operation, then the wind turbine is recorded as a normal wind turbine, and the adjustment parameter matching module is executed; if the operation evaluation information of a certain wind turbine is abnormal operation, then the wind turbine is abnormally operated. The unit is recorded as an abnormal wind turbine unit, and a performance analysis module is executed; the adjustment parameter matching module is used to set a verification period, obtain the operating constraint parameters of the wind turbine unit during the verification period for analysis, obtain the pre-adjustment parameter set of the wind turbine unit, and count the electrical performance parameters of each normal wind turbine unit to correct the pre-adjustment parameter set of the wind turbine unit, obtain the execution adjustment parameter set of each normal wind turbine unit, and perform performance adjustment on each normal wind turbine unit according to the execution adjustment parameter set of each normal wind turbine unit; the performance analysis module is used to transmit the inspection verification signal to the equipment quality inspection control center through the central controller, and perform inspection analysis on the abnormal wind turbine units, build a performance screening order for the abnormal units, and display reminders through the management terminal.

[0020] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0021] 1. The present invention provides a method for improving the performance of a wind turbine based on data analysis. The method obtains and analyzes the operating data of the wind turbine to obtain wind turbine operation evaluation indicators and wind turbine operation evaluation information, and adjusts the performance of the wind turbine and evaluates the mechanical performance parameters according to the evaluation level, thereby dynamically adjusting the operating state of the wind turbine, thereby achieving accurate evaluation and targeted optimization of the operating state of the wind turbine, and effectively solving the problems of single evaluation parameters and lack of targeted adjustment methods in the prior art.

[0022] 2. The present invention sets a performance verification period for the wind turbine, obtains the grid demand parameters and external influence parameters in the period, and analyzes them in combination with the preset standard set in the database to obtain the wind turbine operation verification index and the wind turbine pre-adjustment parameter set, thereby dynamically matching the adjustment requirements of the wind turbine, thereby achieving the improvement of the adaptive performance of the wind turbine under complex environmental conditions.

[0023] 3. By analyzing the pre-adjustment parameter set and the adaptation parameter set of the wind turbine, the execution adjustment parameter set is obtained, and the performance of the wind turbine is adjusted, thereby optimizing the electrical and mechanical performance of the wind turbine, thereby achieving improved operating efficiency of the wind turbine and extended equipment life. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a method for improving wind turbine performance based on data analysis provided in an embodiment of the present application;

[0025] Figure 2 A schematic structural diagram of a wind turbine performance improvement system based on data analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application provide a method and system for improving the performance of a wind turbine generator set based on data analysis, thereby solving the problems of single evaluation parameters and lack of targeted adjustment methods in the prior art, and achieving accurate evaluation and targeted optimization of the operating status of the wind turbine generator set.

[0027] The technical solution in the embodiment of the present application is to solve the above-mentioned problems of single evaluation parameters and lack of pertinence in the adjustment method. The overall idea is as follows: through S1, the operation data of each wind turbine is obtained and analyzed to obtain the operation evaluation index of each wind turbine, and the operation evaluation information of each wind turbine is obtained according to the operation evaluation index of each wind turbine; S2, according to the operation evaluation information of each wind turbine, a judgment is made. If the operation evaluation information of a wind turbine is normal operation, the wind turbine is recorded as a normal wind turbine and S3 is executed. If the operation evaluation information of a wind turbine is abnormal operation, the wind turbine is recorded as an abnormal wind turbine and S4 is executed; S3, verification is set During the verification period, the wind turbine operating constraint parameters are obtained for analysis to obtain the pre-adjustment parameter set of the wind turbine, and the electrical performance parameters of each normal wind turbine are counted to correct the pre-adjustment parameter set of the wind turbine to obtain the execution adjustment parameter set of each normal wind turbine, and the performance of each normal wind turbine is adjusted according to the execution adjustment parameter set of each normal wind turbine; S4, the inspection verification signal is transmitted to the equipment quality inspection control center through the central controller, and the abnormal wind turbines are inspected and analyzed, the performance screening order of the abnormal units is constructed, and the management terminal is used to display reminders, so as to achieve accurate evaluation and targeted optimization of the operating status of the wind turbines.

[0028] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] like Figure 1 As shown, it is a flow chart of a method for improving the performance of a wind turbine based on data analysis provided by an embodiment of the present application, the method comprising the following steps: S1, obtaining and analyzing the operating data of each wind turbine, obtaining the operating evaluation index of each wind turbine, and obtaining the operating evaluation information of each wind turbine according to the operating evaluation index of each wind turbine; S2, judging according to the operating evaluation information of each wind turbine, if the operating evaluation information of a certain wind turbine is normal operation, then recording the wind turbine as a normal wind turbine, and executing S3, if the operating evaluation information of a certain wind turbine is abnormal operation, then recording the wind turbine as an abnormal wind turbine, and executing S4. Execute S4; S3, set a verification period, obtain the wind turbine operating constraint parameters during the verification period for analysis, obtain the wind turbine pre-adjustment parameter set, and count the electrical performance parameters of each normal wind turbine to correct the wind turbine pre-adjustment parameter set to obtain the execution adjustment parameter set of each normal wind turbine, and adjust the performance of each normal wind turbine according to the execution adjustment parameter set of each normal wind turbine; S4, transmit the inspection verification signal to the equipment quality inspection control center through the central controller, and conduct inspection analysis on abnormal wind turbines, build a performance screening order for abnormal units, and display reminders through the management terminal.

[0030] In this embodiment, by real-time monitoring and analysis of the operating data of wind turbines, the present invention can accurately evaluate the performance status of each wind turbine, and timely discover and solve potential performance bottlenecks. Through data analysis, accurate prediction and diagnosis of wind turbine performance is achieved, unnecessary inspections and maintenance times are reduced, and operation and maintenance costs are reduced. Data analysis is combined with wind turbine performance management to achieve intelligent management of wind farms. Through real-time monitoring and data analysis, wind farm managers can grasp the performance status of wind turbines in real time and make more scientific and reasonable decisions.

[0031] According to the summary of power loss of the wind farm from January 2022 to December 2023 for a total of 12 months (power loss data is all data of 50 units). During the 12-month period of the summary data, due to 259 failures of the main control itself, the downtime was 1,039 hours, and the power loss was 538,000 kW.h. As shown in Table 1:

[0032] Table 1 Summary of failure losses

[0033]

[0034]

[0035] Depend on Figure 1 It can be seen that the wind turbines are aging and main control failures occur frequently, which seriously affect the operation of the wind turbines and cause huge power losses. Therefore, wind turbine maintenance is needed to improve the performance of the wind turbines.

[0036] Furthermore, the operating data of each wind turbine is obtained and analyzed to obtain an operating evaluation index of each wind turbine, specifically including: obtaining the operating data of each wind turbine, including the generator output power, power production speed, instantaneous energy efficiency ratio and power generation frequency of each wind turbine; obtaining the operating data rated set preset in the database, including the generator output power rated value, power production speed rated value, instantaneous energy efficiency ratio rated value and power generation frequency rated value; analyzing the operating data of each wind turbine with the operating data rated set to obtain the operating evaluation index of each wind turbine; the operating evaluation index of each wind turbine is used to characterize the operating effect of the wind turbine.

[0037] In this embodiment, the power generation frequency refers to the number of times the current and voltage change periodically per unit time, usually in Hertz (Hz), and the power production rate refers to the power generated per unit time, that is, the instantaneous value of electric power (in watts, W).

[0038] The operation evaluation index of each wind turbine is obtained by:

[0039]

[0040] Where Y i represents the operation evaluation index of the i-th wind turbine, i represents the number of the wind turbine, i=1,2,...,i max ,i max Represents the total number of wind turbines, W i represents the generator output power of the i-th wind turbine, ΔW1 represents the rated value of the generator output power, V i represents the power generation speed of the i-th wind turbine, ΔV1 represents the rated value of the power generation speed, B i represents the instantaneous energy efficiency ratio of the i-th wind turbine, ΔB1 ​​represents the instantaneous energy efficiency ratio rated value, FB i represents the generating frequency of the i-th wind turbine, ΔFB1 represents the rated value of the generating frequency, e represents the natural constant, α1 represents the influence weight of the generator output power, α2 represents the influence weight of the electric energy output speed, α3 represents the influence weight of the instantaneous energy efficiency ratio, and α4 represents the influence weight of the generating frequency.

[0041] It should be noted that the operating data of each wind turbine (generator output power, power output speed, instantaneous energy efficiency ratio and power generation frequency) can be measured and obtained through electrical parameter measurement analyzers and wind turbine parameter measurement instruments. The instantaneous energy efficiency ratio can be directly obtained by using a power analyzer (such as METRAHIT ENERGY and other types of power analyzers) to obtain the instantaneous power of the equipment at a certain moment and calculate the instantaneous energy efficiency ratio.

[0042] The operation evaluation index of each wind turbine is obtained by analyzing the operation data of the wind turbine, taking into account the mutual influence relationship between these parameters. For example, when the output power of the generator increases, the electric energy generated per unit time increases, that is, the speed of electric energy output is accelerated. If the output power is increased while maintaining high efficiency, the instantaneous energy efficiency ratio will also increase accordingly. The increase in output power leads to faster electric energy output. When the wind speed is suitable and the generator is in the best working state, the instantaneous energy efficiency ratio increases, and the output power and electric energy output speed increase, and the power generation frequency increases.

[0043] The weight acquisition method, the generator output power influence weight, the electric energy output speed influence weight, the instantaneous energy efficiency ratio influence weight and the power generation frequency influence weight can be obtained through the database, for example, the preset generator output power, electric energy output speed, instantaneous energy efficiency ratio and power generation frequency in the database are respectively obtained and matched with the current generator output power, electric energy output speed, instantaneous energy efficiency ratio and power generation frequency to obtain the preset generator output power, electric energy output speed, instantaneous energy efficiency ratio and power generation frequency corresponding influence weights as the generator output power influence weight, electric energy output speed influence weight, instantaneous energy efficiency ratio influence weight and power generation frequency influence weight. Among them, the matching process is as follows: for example, the preset generator output power intervals in the database and the generator output power influence weights corresponding to the generator output power intervals are obtained, and the real-time generator output power is matched with the preset generator output power intervals. If the real-time generator output power is within a preset generator output power interval, the generator output power influence weight corresponding to the generator output power interval is obtained as the generator output power influence weight corresponding to the real-time generator output power. For example, the preset electric energy production speed intervals in the database and the electric energy production speed influence weights corresponding to the electric energy production speed intervals are obtained, and the real-time electric energy production speed is matched with the preset electric energy production speed intervals. If the real-time electric energy production speed is within a preset electric energy production speed interval, the electric energy production speed influence weight corresponding to the electric energy production speed interval is obtained as the electric energy production speed influence weight corresponding to the real-time electric energy production speed. For example, the preset instantaneous energy efficiency ratio intervals in the database and the instantaneous energy efficiency ratio influence weights corresponding to the instantaneous energy efficiency ratio intervals are obtained, and the real-time instantaneous energy efficiency ratio is matched with the preset instantaneous energy efficiency ratio intervals. If the real-time instantaneous energy efficiency ratio is within a preset instantaneous energy efficiency ratio interval, the instantaneous energy efficiency ratio influence weight corresponding to the instantaneous energy efficiency ratio interval is obtained as the instantaneous energy efficiency ratio influence weight corresponding to the real-time instantaneous energy efficiency ratio. For example, the preset power generation frequency intervals in the database and the power generation frequency impact weights corresponding to the power generation frequency intervals are obtained, and the real-time power generation frequency is matched with the preset power generation frequency intervals. If the real-time power generation frequency is within a preset power generation frequency interval, the power generation frequency impact weight corresponding to the power generation frequency interval is obtained as the power generation frequency impact weight corresponding to the real-time power generation frequency.

[0044] Furthermore, operation evaluation information of each wind turbine is obtained according to the operation evaluation index of each wind turbine. The specific method is: obtain the wind turbine operation evaluation threshold preset in the database, and match it with the operation evaluation index of each wind turbine. If the operation evaluation index of a wind turbine is above the wind turbine operation evaluation threshold, then the operation evaluation information of the wind turbine is normal operation. If the operation evaluation index of a wind turbine is less than the wind turbine operation evaluation threshold, then the operation evaluation information of the wind turbine is abnormal operation.

[0045] In this embodiment, by matching the operation evaluation index of each wind turbine set with the preset wind turbine set operation evaluation threshold, it is possible to accurately identify which wind turbine sets are in normal operation and which are abnormal, providing a basis for subsequent performance optimization or fault handling.

[0046] Furthermore, the operation constraint parameters of the wind turbine during the verification period are obtained for analysis to obtain a pre-adjustment parameter set of the wind turbine. The specific method is as follows: obtain the operation constraint parameters of the wind turbine during the verification period, including grid demand parameters and external influence parameters; grid demand parameters include frequency, maximum voltage, average power factor, standby capacity call rate and dispatch load demand change rate measured by the grid; external influence parameters include average environmental wind speed, average environmental humidity, average environmental temperature and average dust concentration; obtain the grid demand rated set preset in the database, including the grid measured frequency rated value, voltage rated value, power factor rated value, standby capacity call rate rated value and dispatch load demand change rate rated value; obtain the external influence parameter reference set preset in the database, including wind speed reference value, environmental humidity reference value, environmental temperature reference value and dust concentration reference value; analyze the grid demand parameters and the grid demand rated set to obtain the grid demand index, analyze the external influence parameters and the external influence parameter reference set to obtain the external influence index, and analyze based on the grid demand index and the external influence index to obtain the wind turbine operation verification index; the wind turbine operation verification index is used to characterize the expected degree of the wind turbine.

[0047] In this embodiment, it should be noted that the frequency, maximum voltage, average power factor, and reserve capacity call rate measured by the power grid refer to the frequency, maximum voltage, average power factor, and reserve capacity call rate obtained by connecting the power grid to the wind turbine generator set and measuring the power grid. The power grid demand parameters can be obtained by measuring an electrical parameter measurement analyzer (such as a frequency meter, a high-voltage voltage measurement instrument, and a power quality monitor) and a wind turbine parameter measurement instrument, and the external influence parameters can be obtained by measuring a temperature and humidity sensor, an anemometer, and a laser dust meter.

[0048] Obtaining the grid demand index, the method includes:

[0049]

[0050]

[0051] In the formula, X1 represents the grid demand index, e represents the natural constant, P represents the frequency measured by the grid, ΔP represents the rated value of the frequency measured by the grid, V max Indicates the highest voltage measured by the power grid, ΔV indicates the rated voltage of the power grid, W y Indicates the average power factor measured in the power grid, ΔWy Indicates the rated value of the power factor measured by the power grid, V b Indicates the reserve capacity call rate measured by the power grid, ΔV b represents the rated value of the reserve capacity call rate measured by the power grid, D represents the dispatching load demand change rate measured by the power grid, and ΔD represents the rated value of the dispatching load demand change rate measured by the power grid.

[0052] It should be noted that the grid demand index is obtained by analyzing the grid demand parameters, taking into account the mutual influence relationship between these parameters. For example, the stability of frequency indirectly affects the voltage level and power factor by affecting the output of the generator set; the fluctuation of the highest voltage affects the average power factor by affecting the reactive power distribution in the grid. The improvement of the average power factor can reduce the burden on the grid and lead to improved frequency and voltage stability. The faster the reserve capacity call rate, the stronger the grid's ability to respond to the load demand change rate, and the better the stable operation of the grid.

[0053] The specific method to obtain the external influence index is as follows:

[0054]

[0055] In the formula, X2 represents the external influence index, e represents the natural constant, S represents the average wind speed of the environment, ΔS represents the wind speed reference value, H2 represents the average humidity of the environment, ΔH2 represents the reference value of the humidity of the environment, and W x Indicates the average ambient temperature, ΔW x Indicates the ambient temperature reference value, ρ s represents the average dust concentration, Δρ s Indicates the reference value of dust concentration.

[0056] By analyzing the external influencing parameters, the external influencing index is obtained, which takes into account the mutual influence relationship between these parameters. For example, an increase in wind speed usually leads to a faster rotation speed of the wind turbine blades, thereby improving the power generation efficiency. Excessive humidity will cause the lubrication performance of wind turbine components (such as bearings and gearboxes) to decrease, increasing the possibility of wear and failure. Humidity may also affect the insulation performance of electrical equipment and increase the risk of electrical failure. High temperature and high humidity environment may aggravate the aging and corrosion of equipment. High temperature will cause thermal expansion and deformation of wind turbine components, affecting mechanical properties and precision. High temperature will also accelerate the oxidation and deterioration of lubricating oil and reduce the lubrication effect. Changes in temperature will affect the sedimentation and suspension state of sand and dust, and then affect the degree of sand and dust erosion on wind turbines. Sand and dust particles will clog the air filter and cooling system of wind turbines, resulting in poor heat dissipation and overheating failures, shortening the life of wind turbines.

[0057] The wind turbine operation verification index is obtained by:

[0058]

[0059] Where X represents the wind turbine operation verification index, e represents the natural constant, X1 represents the grid demand index, and X2 represents the external influence index.

[0060] Furthermore, the wind turbine pre-adjustment parameter set specifically comprises the following steps: obtaining the operation verification index intervals of each wind turbine preset in the database and the ideal parameter set of the wind turbine corresponding to each wind turbine operation verification index interval; if the wind turbine operation verification index is within a preset wind turbine operation verification index interval, then obtaining the ideal parameter set of the wind turbine corresponding to the wind turbine operation verification index interval as the wind turbine pre-adjustment parameter set; the wind turbine pre-adjustment parameter set comprises: the pre-adjusted blade angle and the pre-adjusted engine speed of the wind turbine.

[0061] Furthermore, the electrical performance parameters of each normal wind turbine are statistically analyzed to correct the pre-adjustment parameter set of the wind turbine to obtain the execution adjustment parameter set of each normal wind turbine, which specifically includes: obtaining the electrical performance parameters of each normal wind turbine, including the average output power of the generator, the average input current fluctuation of the converter, the average input voltage fluctuation of the converter and the average reactive power of each normal wind turbine during the verification period; obtaining the electrical performance verification parameter set preset in the database, including the rated value of the generator output power, the allowable value of the input current fluctuation of the converter, the allowable value of the voltage fluctuation rate and the rated value of the reactive power; analyzing the electrical performance parameters of each normal wind turbine with the electrical performance rated parameter set to obtain the performance evaluation index of each normal wind turbine; analyzing according to the performance evaluation index of each normal wind turbine to obtain the execution adjustment parameter set of each normal wind turbine; the performance evaluation index of each normal wind turbine is used to characterize the actual operating performance of each normal wind turbine.

[0062] Furthermore, the performance evaluation index of each normal wind turbine is obtained, and the specific method includes:

[0063]

[0064] It should be noted that YC j represents the performance evaluation index of the jth normal wind turbine, j represents the number of the normal wind turbine, j = 1, 2, ..., J, J represents the total number of normal wind turbines, CW j represents the average output power of the generator of the jth normal wind turbine, ΔCW1 represents the rated output power of the generator, and RI j represents the average input current fluctuation of the jth wind turbine in normal condition, ΔRI1 represents the allowable value of the input current fluctuation of the converter, RV jrepresents the average input voltage fluctuation of the converter of the jth normal wind turbine, ΔRV represents the allowable value of the converter input voltage fluctuation rate, WL j represents the average reactive power of the jth normal wind turbine, ΔWL represents the reactive power rating, ε1 represents the influence weight of the average output power of the generator, ε2 represents the influence weight of the current fluctuation, ε3 represents the influence weight of the voltage fluctuation rate, and ε4 represents the influence weight of the reactive power

[0065] In this embodiment, the electrical performance parameters of each normal wind turbine set can be measured and obtained by an electrical parameter measurement and analysis instrument and a wind turbine parameter measurement instrument. The performance evaluation indicators of each normal wind turbine set are obtained by analyzing the electrical performance parameters of each normal wind turbine set, taking into account the mutual influence relationship between these parameters, for example: an increase in the average output power of the generator will directly lead to an increase in the average input current fluctuation of the converter, and indirectly cause grid voltage fluctuations, thereby affecting the stability of the average input voltage of the converter; and the increase in current fluctuations reflects the instability of the generator output power, and will also increase the demand for reactive power in the grid, resulting in an increase in average reactive power; the fluctuation of the input voltage indirectly causes output power fluctuations by affecting the working state of the converter, and the voltage fluctuation is closely related to the reactive power demand, and voltage instability will cause an increase in reactive power loss.

[0066] The influence weight of the generator average output power, the influence weight of the current fluctuation amount, the influence weight of the voltage fluctuation rate and the influence weight of the reactive power can be obtained through a database. For example, the preset generator average output power, current fluctuation amount representation, voltage fluctuation rate and reactive power in the database are respectively obtained and matched with the current generator average output power, current fluctuation amount representation, voltage fluctuation rate and reactive power to obtain the preset influence weights corresponding to the generator average output power, current fluctuation amount representation, voltage fluctuation rate and reactive power as the generator average output power influence weight, current fluctuation amount influence weight, voltage fluctuation rate influence weight and reactive power influence weight.

[0067] It should be noted that the specific matching process is: obtaining the average output power intervals of each generator preset in the database and the average output power influence weights of each generator corresponding to the average output power intervals of each generator, matching the real-time average output power of the generator with the preset average output power intervals of each generator, and if the real-time average output power of the generator is within a preset average output power interval of the generator, then obtaining the average output power influence weight of the generator corresponding to the average output power interval of the generator as the average output power influence weight of the generator corresponding to the real-time average output power of the generator. Obtaining the current fluctuation intervals preset in the database and the current fluctuation influence weights corresponding to each current fluctuation interval, matching the real-time current fluctuation with the preset current fluctuation intervals, and if the real-time current fluctuation is within a preset current fluctuation interval, then obtaining the current fluctuation influence weight corresponding to the current fluctuation interval as the current fluctuation influence weight corresponding to the real-time current fluctuation. Obtain each preset voltage fluctuation rate interval in the database and the voltage fluctuation rate influence weight corresponding to each voltage fluctuation rate interval, match the real-time voltage fluctuation rate with each preset voltage fluctuation rate interval, if the real-time voltage fluctuation rate is within a preset voltage fluctuation rate interval, obtain the voltage fluctuation rate influence weight corresponding to the voltage fluctuation rate interval as the voltage fluctuation rate influence weight corresponding to the real-time voltage fluctuation rate. Obtain each preset reactive power interval in the database and the reactive power influence weight corresponding to each reactive power interval, match the real-time reactive power with each preset reactive power interval, if the real-time reactive power is within a preset reactive power interval, obtain the reactive power influence weight corresponding to the reactive power interval as the reactive power influence weight corresponding to the real-time reactive power.

[0068] Furthermore, analysis is performed based on the performance evaluation indicators of each normal wind turbine set to obtain an execution adjustment parameter set for each normal wind turbine set, specifically including: obtaining each performance evaluation indicator interval preset in the database and an operation correction parameter set corresponding to each performance evaluation indicator interval, if the performance evaluation indicator of a normal wind turbine set is within a certain performance evaluation indicator interval, then obtaining the operation correction parameter set corresponding to the performance evaluation indicator interval as the operation correction parameter set of the normal wind turbine set; the execution adjustment parameter set of the normal wind turbine set includes the blade angle correction value and the engine speed correction value of each normal wind turbine set; according to the pre-adjustment parameter set of the wind turbine set, and fusion processing is performed one by one with the operation correction parameter set of the normal wind turbine set, to obtain the execution adjustment parameter set of each normal wind turbine set, the execution adjustment parameter set includes the target execution control blade angle and the target execution control engine speed.

[0069] In the present embodiment, it should be noted that, according to the pre-adjustment parameter set of the wind turbine, the execution adjustment parameter set of each normal wind turbine is obtained by fusing them one by one with the operation correction parameter set of the normal wind turbine. For example, the pre-adjustment blade angle and pre-adjustment engine speed of the wind turbine are respectively subtracted from the corresponding blade angle correction value and engine speed correction value of each normal wind turbine to obtain the target execution control blade angle and target execution control engine speed.

[0070] Furthermore, the abnormal wind turbines are inspected and analyzed, and a performance screening order for the abnormal turbines is constructed, and displayed and reminded through the management terminal. The specific method is: use drones to collect multi-dimensional images of each abnormal wind turbine, and extract a collection of appearance defect data of each abnormal wind turbine, the appearance defect data collection includes the maximum blade depression depth, the maximum blade depression area, the number of blade cracks and the maximum blade crack length of each abnormal wind turbine; based on the appearance defect data collection of each abnormal wind turbine, analysis is performed to obtain the appearance defect index of each abnormal wind turbine; based on the operation evaluation index of the abnormal wind turbine, the abnormal wind turbine operation evaluation index corresponding to each abnormal wind turbine is extracted, and combined with the appearance defect index of the abnormal wind turbine, the inspection priority index of the abnormal wind turbine is obtained; according to the inspection priority index of the abnormal wind turbine, it is arranged in descending order to obtain the performance screening order of the abnormal turbine and displayed and reminded through the management terminal.

[0071] In this embodiment, it should be noted that, by identifying relevant parameters in the sorted pictures, a collection of appearance defect data of abnormal wind turbines can be obtained, which can be automatically acquired through image processing software (such as Adobe Photoshop, GI MP).

[0072] The appearance defect index of each abnormal wind turbine is obtained by:

[0073]

[0074] Where DQ k represents the appearance defect index of the kth abnormal wind turbine, DW k represents the maximum concave depth of the blade of the kth abnormal wind turbine, k represents the number of the abnormal wind turbine, k=1,2,...,k max , k max represents the total number of abnormal wind turbines, ΔDW represents the reference value of the maximum blade depression depth, DI represents the maximum blade depression area of ​​the kth abnormal wind turbine, and ΔDI k Indicates the reference value of the maximum concave area of ​​the blade, DV k represents the number of blade cracks of the kth abnormal wind turbine, ΔDV represents the reference value of the number of blade cracks, and DPk represents the maximum crack length of the blade of the kth abnormal wind turbine, and ΔDP represents the reference value of the maximum crack length of the blade.

[0075] It should be noted that the reference value of the maximum depression depth of the blade, the reference value of the maximum depression area of ​​the blade, the reference value of the number of cracks on the blade and the reference value of the maximum crack length of the blade can be obtained through a database.

[0076] The inspection priority index of the abnormal wind turbine is obtained by combining the appearance defect index of the abnormal wind turbine with that of the abnormal wind turbine. The appearance defect index of the abnormal wind turbine can be matched with the preset appearance defect index intervals by obtaining the preset appearance defect index intervals and the inspection priority indexes corresponding to the appearance defect indexes in the database. If the appearance defect index of the abnormal wind turbine is within a preset appearance defect index interval, the inspection priority index corresponding to the appearance defect index interval is obtained as the inspection priority index of the abnormal wind turbine.

[0077] It should be noted that drones are used to collect multi-dimensional pictures of abnormal wind turbines, and a collection of appearance defect data of abnormal wind turbines is extracted from them. The specific method is: use the camera on the drone to take pictures to capture image information of the abnormal wind turbines, and perform sorting and other sorting operations on the taken pictures. According to the sorted pictures, relevant parameters in the pictures are identified to obtain a collection of appearance defect data of abnormal wind turbines.

[0078] like Figure 2As shown, it is a structural schematic diagram of a wind turbine performance improvement system based on data analysis provided in an embodiment of the present application. A wind turbine performance improvement system based on data analysis provided in an embodiment of the present application includes: a wind turbine operation evaluation module, an execution judgment module, an adjustment parameter matching module and a performance analysis module; wherein the wind turbine operation evaluation module is used to obtain and analyze the operation data of each wind turbine, obtain the operation evaluation index of each wind turbine, and obtain the operation evaluation information of each wind turbine according to the operation evaluation index of each wind turbine; the execution judgment module is used to judge according to the operation evaluation information of each wind turbine. If the operation evaluation information of a certain wind turbine is normal operation, the wind turbine is recorded as a normal wind turbine, and the adjustment parameter matching module is executed. If a certain If the wind turbine operation evaluation information is abnormal operation, the wind turbine will be recorded as an abnormal wind turbine and the performance analysis module will be executed; the adjustment parameter matching module is used to set the verification period, obtain the wind turbine operation constraint parameters during the verification period for analysis, obtain the wind turbine pre-adjustment parameter set, and count the electrical performance parameters of each normal wind turbine to correct the wind turbine pre-adjustment parameter set to obtain the execution adjustment parameter set of each normal wind turbine, and perform performance adjustment on each normal wind turbine according to the execution adjustment parameter set of each normal wind turbine; the performance analysis module is used to transmit the inspection verification signal to the equipment quality inspection control center through the central controller, and perform inspection analysis on the abnormal wind turbines, build the performance screening order of the abnormal units and display reminders through the management terminal.

[0079] To sum up, this embodiment obtains and analyzes the operating data of the wind turbine set to obtain the wind turbine set operation evaluation index and wind turbine set operation evaluation information, and performs performance adjustment and mechanical performance parameter evaluation on the wind turbine set according to the evaluation level, thereby dynamically adjusting the operating status of the wind turbine set, thereby achieving accurate evaluation and targeted optimization of the operating status of the wind turbine set, and effectively solving the problems of single evaluation parameters and lack of targeted adjustment methods in the prior art.

[0080] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0084] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for improving wind turbine performance based on data analysis, characterized in that: The following steps are involved: S1, obtaining and analyzing the operation data of each wind turbine generator set to obtain the operation evaluation index of each wind turbine generator set, and obtaining the operation evaluation information of each wind turbine generator set according to the operation evaluation index of each wind turbine generator set; S2, judging according to the operation evaluation information of each wind turbine set, if the operation evaluation information of a wind turbine set is normal operation, then the wind turbine set is recorded as a normal wind turbine set, and S3 is executed; if the operation evaluation information of a wind turbine set is abnormal operation, then the wind turbine set is recorded as an abnormal wind turbine set, and S4 is executed; S3, setting a verification period, obtaining the wind turbine operating constraint parameters during the verification period for analysis, obtaining a pre-adjustment parameter set of the wind turbine, and statistically analyzing the electrical performance parameters of each normal wind turbine to correct the pre-adjustment parameter set of the wind turbine to obtain an execution adjustment parameter set of each normal wind turbine, and adjusting the performance of each normal wind turbine according to the execution adjustment parameter set of each normal wind turbine; S4, transmits the inspection verification signal to the equipment quality inspection control center through the central controller, and conducts inspection analysis on the abnormal wind turbines, builds a performance screening order for the abnormal units and displays reminders through the management terminal.

2. A method for improving wind turbine performance based on data analysis as claimed in claim 1, characterized in that: The operation data of each wind turbine generator set is obtained and analyzed to obtain the operation evaluation index of each wind turbine generator set, specifically including: Obtain the operating data of each wind turbine, including the generator output power, power output speed, instantaneous energy efficiency ratio and power generation frequency of each wind turbine; Obtaining a preset operating data rating set in a database, including a generator output power rating, an electric energy production speed rating, an instantaneous energy efficiency ratio rating, and a power generation frequency rating; Analyze the operating data of each wind turbine generator set with the rated set of operating data to obtain the operating evaluation index of each wind turbine generator set; The wind turbine operation evaluation index is used to characterize the operation effect of the wind turbine.

3. A method for improving wind turbine performance based on data analysis as claimed in claim 1, characterized in that: The specific method for obtaining the operation evaluation information of each wind turbine generator set according to the operation evaluation index of each wind turbine generator set is as follows: The wind turbine operation evaluation threshold preset in the database is obtained, and matched with each wind turbine operation evaluation index. If a wind turbine operation evaluation index is above the wind turbine operation evaluation threshold, the wind turbine operation evaluation information is normal operation. If a wind turbine operation evaluation index is less than the wind turbine operation evaluation threshold, the wind turbine operation evaluation information is abnormal operation.

4. A method for improving wind turbine performance based on data analysis as claimed in claim 1, characterized in that: The wind turbine operating constraint parameters under the verification period are obtained and analyzed to obtain the wind turbine pre-adjustment parameter set. The specific method is as follows: Obtain wind turbine operation constraint parameters during the verification period, including grid demand parameters and external impact parameters; The grid demand parameters include the frequency, maximum voltage, average power factor, reserve capacity call rate and dispatch load demand change rate measured by the grid; The external influencing parameters include the average wind speed, average humidity, average temperature and average dust concentration; Obtaining a preset grid demand rating set in a database, including a grid-measured frequency rating, a voltage rating, a power factor rating, a reserve capacity call rate rating, and a dispatch load demand change rate rating; Obtaining a reference set of external influence parameters preset in the database, including a wind speed reference value, an ambient humidity reference value, an ambient temperature reference value, and a dust concentration reference value; Analyze the grid demand parameters and the grid demand rated set to obtain the grid demand index, analyze the external influence parameters and the external influence parameter reference set to obtain the external influence index, and analyze the grid demand index and the external influence index to obtain the wind turbine operation verification index; The wind turbine generator set operation verification index is used to characterize the expected degree of the wind turbine generator set.

5. A method for improving wind turbine performance based on data analysis as claimed in claim 4, characterized in that: The wind turbine pre-adjustment parameter set comprises the following specific steps: Obtaining the wind turbine operation verification index intervals preset in the database and the ideal wind turbine parameter sets corresponding to the wind turbine operation verification index intervals; if the wind turbine operation verification index is within a preset wind turbine operation verification index interval, obtaining the wind turbine ideal parameter set corresponding to the wind turbine operation verification index interval as the wind turbine pre-adjustment parameter set; The wind turbine pre-adjustment parameter set includes: a pre-adjusted blade angle and a pre-adjusted engine speed of the wind turbine.

6. A method for improving wind turbine performance based on data analysis as claimed in claim 1, characterized in that: The statistical electrical performance parameters of each normal wind turbine are used to correct the pre-adjustment parameter set of the wind turbine to obtain the execution adjustment parameter set of each normal wind turbine, specifically including: Obtain the electrical performance parameters of each normal wind turbine generator set, including the average generator output power, average converter input current fluctuation, average converter input voltage fluctuation and average reactive power of each normal wind turbine generator set during the verification period; Obtaining a preset electrical performance verification parameter set in a database, including a generator output power rating, a converter input current fluctuation allowable value, a voltage fluctuation rate allowable value, and a reactive power rating; Analyze the electrical performance parameters of each normal wind turbine generator set and the electrical performance rated parameter set to obtain the performance evaluation index of each normal wind turbine generator set; Analyze the performance evaluation index of each normal wind turbine generator set to obtain the execution adjustment parameter set of each normal wind turbine generator set; The performance evaluation index of each normal wind turbine generator set is used to characterize the actual operating performance of each normal wind turbine generator set.

7. A method for improving wind turbine performance based on data analysis as claimed in claim 6, characterized in that: The specific method for obtaining the performance evaluation index of each normal wind turbine is as follows: It should be noted that YC j represents the performance evaluation index of the jth normal wind turbine, j represents the number of the normal wind turbine, j = 1, 2, ..., J, J represents the total number of normal wind turbines, CW j represents the average output power of the generator of the jth normal wind turbine, ΔCW1 represents the rated output power of the generator, and RI j represents the average input current fluctuation of the jth wind turbine in normal condition, ΔRI1 represents the allowable value of the input current fluctuation of the converter, RV j represents the average input voltage fluctuation of the converter of the jth normal wind turbine, ΔRV represents the allowable value of the converter input voltage fluctuation rate, WL j represents the average reactive power of the jth normal wind turbine, ΔWL represents the reactive power rating, ε1 represents the influence weight of the average output power of the generator, ε2 represents the influence weight of the current fluctuation, ε3 represents the influence weight of the voltage fluctuation rate, and ε4 represents the influence weight of the reactive power.

8. A method for improving wind turbine performance based on data analysis as claimed in claim 6, characterized in that: The analysis is performed based on the performance evaluation index of each normal wind turbine generator set to obtain the execution adjustment parameter set of each normal wind turbine generator set, specifically including: Obtain each performance evaluation index interval preset in the database and the operation correction parameter set corresponding to each performance evaluation index interval. If the performance evaluation index of a normal wind turbine generator set is within a certain performance evaluation index interval, obtain the operation correction parameter set corresponding to the performance evaluation index interval as the operation correction parameter set of the normal wind turbine generator set. The execution adjustment parameter set of the normal wind turbine generator set includes a blade angle correction value and an engine speed correction value of each normal wind turbine generator set; According to the pre-adjustment parameter set of the wind turbine generator set, the execution adjustment parameter set of each normal wind turbine generator set is obtained by fusing them one by one with the operation correction parameter set of the normal wind turbine generator set. The execution adjustment parameter set includes a target execution control blade angle and a target execution control engine speed.

9. A method for improving wind turbine performance based on data analysis as claimed in claim 1, characterized in that: The inspection and analysis of abnormal wind turbines is performed, and a performance screening sequence of abnormal turbines is constructed and displayed and reminded through a management terminal. The specific method is as follows: Using drones to collect multi-dimensional images of each abnormal wind turbine, and extracting a collection of appearance defect data of each abnormal wind turbine, wherein the collection of appearance defect data includes a maximum blade depression depth, a maximum blade depression area, a number of blade cracks, and a maximum blade crack length of each abnormal wind turbine; Based on the appearance defect data collection of each abnormal wind turbine, an appearance defect index of each abnormal wind turbine is obtained; Based on the abnormal wind turbine operation evaluation index, the abnormal wind turbine operation evaluation index corresponding to each abnormal wind turbine is extracted, and the inspection priority index of the abnormal wind turbine is obtained by combining the appearance defect index of the abnormal wind turbine. According to the inspection priority index of abnormal wind turbines and arranging them in descending order, the performance screening order of abnormal units is obtained and displayed and reminded through the management terminal.

10. A system using a method for improving wind turbine performance based on data analysis as claimed in any one of claims 1 to 9, characterized in that: include: Wind turbine operation evaluation module, execution judgment module, adjustment parameter matching module and performance analysis module; The wind turbine operation evaluation module is used to obtain and analyze the operation data of each wind turbine to obtain the operation evaluation index of each wind turbine, and obtain the operation evaluation information of each wind turbine according to the operation evaluation index of each wind turbine; The execution judgment module is used to make a judgment based on the operation evaluation information of each wind turbine set. If the operation evaluation information of a wind turbine set is normal operation, the wind turbine set is recorded as a normal wind turbine set, and the adjustment parameter matching module is executed; if the operation evaluation information of a wind turbine set is abnormal operation, the wind turbine set is recorded as an abnormal wind turbine set, and the performance analysis module is executed; The adjustment parameter matching module is used to set a verification period, obtain the wind turbine operating constraint parameters during the verification period for analysis, obtain a wind turbine pre-adjustment parameter set, and count the electrical performance parameters of each normal wind turbine to correct the wind turbine pre-adjustment parameter set to obtain an execution adjustment parameter set of each normal wind turbine, and perform performance adjustment on each normal wind turbine according to the execution adjustment parameter set of each normal wind turbine; The performance analysis module is used to transmit the inspection verification signal to the equipment quality inspection control center through the central controller, and perform inspection analysis on abnormal wind turbines, establish a performance screening order for abnormal units, and display reminders through the management terminal.

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