Wind turbine generator power control performance evaluation method, device, equipment and medium

Abstract

The invention discloses a wind turbine generator power control performance evaluation method and device, equipment and a medium, and belongs to the field of new energy access and control. The method comprises the following steps: constructing a joint simulation model, performing verification stage simulation based on an actually measured wind speed, comparing model simulation data with test data, adjusting model parameters to meet a preset deviation threshold value, and forming a power control characteristic simulation universal model; and for a to-be-evaluated target model, modifying component parameters of the general model to obtain an evaluation model simulation model, further carrying out evaluation stage simulation, and determining a power control characteristic evaluation result of the target model according to a comparison result of evaluation simulation data and an evaluation threshold value. According to the method, the evaluation problem caused by many manufacturers and various accessories of the existing wind turbine generator is solved, the rapid and accurate evaluation of the power control characteristics is realized, and the requirement of the grid-connected guide rule on the friendly supporting capability of the power grid is met.

Description

Technical Field

[0001] The present invention belongs to the field of new energy access and control, and specifically relates to a method, device, equipment and medium for evaluating the power control performance of a wind turbine generator set. Background Art

[0002] The intermittent and random nature of wind energy presents challenges for wind turbine power control and output stability. The widespread penetration of wind turbines in the power system has reduced system inertia and increased frequency instability. Existing grid-connection guidelines require wind turbines to possess grid-friendly active support capabilities, such as inertia and primary frequency regulation, to support frequency through power control. Furthermore, the need for rapid response and tracking of dispatch instructions places even higher demands on wind turbine power response and accurate control. Therefore, it is necessary to evaluate wind turbine power control characteristics to confirm their appropriate power control capabilities.

[0003] Currently, wind turbines are manufactured by multiple manufacturers and have a wide variety of core components, which poses a significant challenge to evaluating wind turbine power control characteristics. To better meet the needs of wind turbine power characteristic evaluation, it is urgent to develop a universal and simple power characteristic evaluation method that can quickly and accurately evaluate wind turbine power control characteristics and promote the safe and stable development of power systems with a high proportion of renewable energy. Summary of the Invention

[0004] The object of the present invention is to provide a method, device, equipment and medium for evaluating the power control performance of a wind turbine generator set, so as to solve or improve the technical problems of evaluating the power control characteristics of a wind turbine generator set in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for evaluating the power control performance of a wind turbine generator system, comprising: Obtain test data of actual wind turbines under measured wind speeds; the test data includes measured wind speeds, actual three-phase currents and voltages, and time series; Determine the joint simulation model of wind turbines; Inputting the measured wind speed into the joint simulation model for model simulation in the verification phase to obtain model simulation data of the wind turbine generator set; wherein the operating conditions of the model simulation in the verification phase include a static error simulation condition of active power setting value control and a dynamic response characteristic simulation condition of reactive power setting value control; the model simulation data includes three-phase current and voltage simulation data and corresponding simulation data time series values, as well as power data determined based on the three-phase current and voltage simulation data, the power data including active power and reactive power; Determining a simulation deviation result of the joint simulation model relative to the actual wind turbine generator system based on the model simulation data and the test data; adjusting model parameters of the joint simulation model according to the simulation deviation result until the simulation deviation result meets a preset deviation threshold, thereby obtaining a general model for simulating power control characteristics; Determine the simulation model of the evaluation model based on the general model for simulating power control characteristics and the target model to be evaluated; The evaluation phase simulation is performed on the simulation model of the evaluation model to obtain evaluation simulation data; the power control characteristic evaluation result of the target model is determined based on the evaluation simulation data; wherein, the operating conditions simulated in the evaluation phase include the active power setting value control static error simulation condition, the active power setting value control dynamic response characteristic simulation condition, the reactive power setting value control static error simulation condition, the reactive power setting value control dynamic response characteristic simulation condition, the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition and the reactive power setting value zero simulation condition.

[0006] Furthermore, the measured wind speed is input into the joint simulation model to perform model simulation in the verification phase to obtain model simulation data of the wind turbine, including: For the active power setpoint control static error simulation condition, the verification phase model simulation is performed as follows: Determine the initial value of the active power set value of the joint simulation model; under the measured wind speed condition, reduce the active power set value from the initial value by a preset step size in each control adjustment cycle to the preset target value; record the model simulation data of each control adjustment cycle; For the reactive power setpoint control dynamic response characteristic simulation condition, the verification stage model simulation is performed as follows: Two adjacent control adjustment cycles are set; in the first control adjustment cycle, the joint simulation model is set to operate in a capacitive reactive mode, and the reactive setting value is set to a first preset proportion of the reactive output capacity; in the second control adjustment cycle, the joint simulation model is set to operate in an inductive reactive mode, and the reactive setting value is set to the first preset proportion of the reactive output capacity; in both the first and second control adjustment cycles, the active power is maintained within the range of the maximum power generation capacity of the wind turbine; and the model simulation data of the first and second control adjustment cycles under the measured wind speed conditions are recorded.

[0007] Furthermore, based on the model simulation data and the test data, the simulation deviation results of the joint simulation model relative to the actual wind turbine are determined, including: Determine, based on the power data and the simulation data sequence value, the time when the power step change starts, the time when the power first reaches a preset proportion of the set value from the initial value, and the time when the power starts to continuously remain within a preset stable error band; Determine the adjustment time of each control adjustment cycle according to the time when the power step change starts and the time when the power starts to be continuously maintained within the preset stable error band; According to the start and end time of the kth adjustment time and the power data during the adjustment time, the average absolute deviation between the model simulation data and the test data during the adjustment time is calculated; Determine a stable operation time period according to the end time of the mth adjustment time and the start time of the m+1th adjustment time, and calculate the average absolute deviation between the model simulation data and the test data in the stable operation time period according to the end time of the mth adjustment time and the start time of the m+1th adjustment time and the power data in the stable operation time period; Calculate the response time of the power in the model simulation data and the test data based on the start time of the power step change and the time when the power first reaches a preset proportion of the set value from the initial value; when the response time of the power in the test data is less than the response time limit, calculate the absolute deviation of the response time between the model simulation data and the test data; when the response time of the power in the test data is greater than or equal to the response time limit, calculate the relative deviation of the response time between the model simulation data and the test data; The overshoot of the model simulation data and the test data is calculated according to the maximum power response value and the set value, and the overshoot deviation of the model simulation data and the test data is calculated according to the overshoot.

[0008] Furthermore, the evaluation model simulation model is simulated in the evaluation phase to obtain evaluation simulation data, including: According to the active power setting value, the static error is controlled by simulating the working condition test to obtain the active absolute static error; according to the active power setting value, the dynamic response characteristic is controlled by simulating the working condition test to obtain the active power stabilization time, fall time and response time; According to the working condition tests of the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition and the reactive power setting value zero simulation condition, the average active power, average reactive power, power factor and power factor deviation corresponding to each working condition test are obtained; According to the reactive power setting value, the static error is controlled by simulating the working condition test to obtain the absolute static error of reactive power; according to the reactive power setting value, the dynamic response characteristic is controlled by simulating the working condition test to obtain the reactive power stabilization time, rise time and response time.

[0009] Furthermore, in the step of determining the evaluation model simulation model based on the power control characteristic simulation general model and the target model to be evaluated, if the components of the target model to be evaluated change, the component parameters in the power control characteristic simulation general model are modified to obtain an evaluation model simulation model consistent with the target model.

[0010] Furthermore, the joint simulation model includes a Bladed mechanical model and a Matlab electrical model.

[0011] Furthermore, based on the evaluation simulation data, the power control characteristic evaluation result of the target model is determined, including: Determine the evaluation threshold of each evaluation simulation data; Each evaluation simulation data is compared with the corresponding evaluation threshold. When any evaluation simulation data does not meet the evaluation threshold, the power control characteristic evaluation result of the target model is judged to be unqualified.

[0012] In a second aspect of the present invention, a device for evaluating the power control performance of a wind turbine generator system is provided, comprising: The first data acquisition module is used to obtain test data of the actual wind turbine under the measured wind speed; the test data includes the measured wind speed, the actual three-phase current and voltage, and the time series; The first model building module is used to determine the joint simulation model of the wind turbine generator system; a second data acquisition module, configured to input the measured wind speed into the joint simulation model for model simulation in the verification phase, thereby obtaining model simulation data of the wind turbine generator set; wherein the operating conditions of the model simulation in the verification phase include a static error simulation condition of active power setting value control and a dynamic response characteristic simulation condition of reactive power setting value control; the model simulation data include three-phase current and voltage simulation data and corresponding simulation data time series values, as well as power data determined based on the three-phase current and voltage simulation data, wherein the power data includes active power and reactive power; A model adjustment module is used to determine a simulation deviation result of the joint simulation model relative to the actual wind turbine generator system based on the model simulation data and the test data; adjust the model parameters of the joint simulation model according to the simulation deviation result until the simulation deviation result meets a preset deviation threshold, thereby obtaining a general model for power control characteristic simulation; The second model building module is used to determine the evaluation model simulation model according to the power control characteristic simulation general model and the target model to be evaluated; An evaluation module is used to simulate the evaluation phase of the evaluation model simulation model to obtain evaluation simulation data; and determine the power control characteristic evaluation result of the target model based on the evaluation simulation data; wherein, the operating conditions simulated in the evaluation phase include the active power setting value control static error simulation condition, the active power setting value control dynamic response characteristic simulation condition, the reactive power setting value control static error simulation condition, the reactive power setting value control dynamic response characteristic simulation condition, the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition, and the reactive power setting value zero simulation condition.

[0013] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the wind turbine power control performance evaluation method as described above.

[0014] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the wind turbine power control performance evaluation method as described above is implemented.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The method proposed in this paper utilizes a co-simulation model combining a Bladed mechanical model with a Matlab electrical model, enabling comprehensive and accurate simulation of both the mechanical and electrical characteristics of a wind turbine. During the verification phase, static error simulations for active power setpoint control and dynamic response simulations for reactive power setpoint control are performed, covering common active and reactive power control conditions encountered in actual wind turbine operation. By inputting measured wind speeds into the co-simulation model, wind turbine model simulation data is obtained and compared with test data. Multiple deviation metrics, such as settling time, mean absolute deviation, absolute deviation of response time, relative deviation of response time, and overshoot deviation, are calculated. This method meticulously reflects the model's simulation performance under different operating conditions. Model parameters are then precisely adjusted until preset deviation thresholds are met, resulting in a universal model for simulating power control characteristics. This method enables rapid and accurate assessment of wind turbine power control characteristics, demonstrates strong universality and simplicity, and provides important technical support for the design, manufacture, operation, and maintenance of wind turbines, with significant significance for promoting the safe and stable development of new energy power systems. The simulation test conditions of the method of the present invention in the evaluation stage include active power set value control, reactive power set value control, maximum inductive reactive power, maximum capacitive reactive power, and reactive power zero, etc., which comprehensively cover various power control scenarios that wind turbines may encounter in actual operation and can accurately evaluate their power control characteristics under different working conditions. The method of the present invention sets a variety of evaluation indicators for different evaluation conditions, including active absolute static error, active power stabilization time, fall time, response time, reactive absolute static error, reactive power stabilization time, rise time, average active power, average reactive power, power factor, and power factor deviation. These indicators can comprehensively measure the power control performance of wind turbines from multiple dimensions, such as static error, dynamic response speed, stability, and power factor, providing strong guarantees for the accuracy and reliability of the evaluation results. When the components of the target model to be evaluated change, it is only necessary to modify the corresponding component parameters in the general power control characteristic simulation model to obtain an evaluation model simulation model consistent with the target model. There is no need to rebuild the entire model, which greatly improves the evaluation efficiency and reduces the evaluation cost. It is especially suitable for the power control characteristic evaluation scenario of wind turbines with multiple manufacturers and a variety of core components. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a method for evaluating wind turbine power control performance according to an embodiment of the present invention; Figure 2 Schematic diagram of the verification and simulation stages in an embodiment of the present invention; Figure 3 This is a comparison chart of active power between model simulation data and test data in an embodiment of the present invention; Figure 4 A reactive power comparison diagram of the model simulation data and the test data in an embodiment of the present invention; Figure 5 This is a time series diagram of active power setting values and simulation values during the static evaluation period of active power setting value control in an embodiment of the present invention; Figure 6 A time series diagram of active power set values and simulation values during the dynamic evaluation of active power set value control in an embodiment of the present invention; Figure 7 60s average active power and reactive power diagram in the maximum inductive reactive mode in an embodiment of the present invention; Figure 8 60s average active power and reactive power diagram in the maximum capacitive reactive mode in an embodiment of the present invention; Figure 9 This is a diagram showing the simulation results of the 60s average active power and reactive power when the reactive power setting value is zero in an embodiment of the present invention; Figure 10 A time series diagram of reactive power set values and simulation values during a static evaluation period of reactive power set value control in an embodiment of the present invention; Figure 11 A time series diagram of reactive power set values and simulation values during a dynamic evaluation period of reactive power set value control in an embodiment of the present invention; Figure 12 This is a structural block diagram of a wind turbine power control performance evaluation device according to an embodiment of the present invention; Figure 13 The figure is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0017] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0018] Example 1 This invention aims to address the current problem of inaccurate and rapid evaluation of wind turbine power control characteristics. It proposes a wind turbine power control performance evaluation method for evaluating the control performance of wind turbine active and reactive setpoints. This method provides a universal and simple power characteristic evaluation method applicable to different manufacturers and different types of core components. Through power characteristic verification and simulation, accurate and rapid simulation of wind turbine power characteristics is achieved, contributing to the efficient development of the wind power industry.

[0019] The wind turbine power characteristic evaluation method provided in this solution includes two stages: verification and evaluation.

[0020] Verification stage: First, a joint simulation model of wind turbine power quality and power control is constructed. The joint simulation model includes a Bladed mechanical model and a Matlab electrical model. The model simulation data under the simulation conditions of active power setting value control static error and reactive power setting value control dynamic response characteristics are obtained. The model simulation data includes three-phase current and voltage simulation values and simulation data time series values, as well as power data determined according to the three-phase current and voltage simulation values. The power data includes active power and reactive power. The model simulation data is compared with the test data to calculate the deviation. The consistency of the simulation model and the type test characteristics is judged based on the deviation. If the deviation is not satisfied, the joint simulation model is modified until the deviation is within the allowable range.

[0021] Evaluation Phase: Based on the verified co-simulation model, the parameters of the changed components are modified to obtain an evaluation model. Simulations are then conducted on the evaluation model for active power setpoint control static error, active power setpoint control dynamic response characteristics, reactive power setpoint control static error, reactive power setpoint control dynamic response characteristics, maximum inductive reactive power, maximum capacitive reactive power, and a reactive power setpoint zero condition. The power control characteristics are evaluated based on the evaluation simulation data.

[0022] In the above scheme, the wind turbine joint simulation model verifies the deviation indicators, including the average absolute deviation within the active power or reactive power control adjustment time, the average absolute deviation during the period from stable operation after the active or reactive power control adjustment time ends to the time before the next control instruction occurs, the absolute deviation / relative deviation of the response time, and the overshoot deviation.

[0023] In the above scheme, the power characteristic evaluation indicators of wind turbines include the maximum absolute static error of active power, active dynamic stabilization time, active dynamic decline time, active dynamic response time, maximum inductive reactive power factor deviation, maximum capacitive reactive power factor deviation, reactive zero power factor deviation, maximum absolute static error of reactive power, reactive dynamic stabilization time, reactive dynamic rise time and reactive dynamic response time, etc.

[0024] like Figure 1 and Figure 2 As shown, a method for evaluating the power control performance of a wind turbine generator system includes the following steps: S1. Obtain test data of an actual wind turbine under a measured wind speed; the test data includes the measured wind speed, actual three-phase current and voltage, and time series.

[0025] It should be noted that to maintain data consistency, the input wind speed of the joint simulation model is the measured wind speed of the actual wind turbine. The model simulation data is the simulation data obtained from the joint simulation model.

[0026] S2. Determine the joint simulation model of the wind turbine.

[0027] Specifically, the co-simulation model includes a Bladed mechanical model and a Matlab electrical model. The Bladed electrical model includes a blade model, hub model, drive train model, tower model, and controller model; the Matlab model includes a power grid model, generator model, transformer model, converter, and its control model.

[0028] S3. Input the measured wind speed into the joint simulation model for verification phase model simulation to obtain model simulation data of the wind turbine generator set; wherein, the working conditions of the verification phase model simulation include active power setting value control static error simulation conditions and reactive power setting value control dynamic response characteristic simulation conditions; the model simulation data include three-phase current and voltage simulation data and corresponding simulation data sequence values, as well as power data determined based on the three-phase current and voltage simulation data, and the power data includes active power and reactive power.

[0029] Specifically, the measured wind speed is input into the joint simulation model to perform model simulation in the verification phase to obtain model simulation data of the wind turbine, including: S31. For the active power setting value control static error simulation working condition, perform the verification phase model simulation as follows: Determine the initial value of the active power setting value of the joint simulation model; under the measured wind speed conditions, reduce the active power setting value from the initial value by a preset step size in each control adjustment cycle to the preset target value; record the model simulation data of the wind turbine group in each control adjustment cycle.

[0030] For example, the initial value of the active power setting value can be set to 1.00 pu, the preset target value can be set to 0.20 pu, and the preset step size can be 0.20 pu; that is, the active power setting value starts from 1.00 pu and decreases to 0.20 pu in steps of 0.20 pu, and each step size lasts for at least 2 minutes.

[0031] It should be noted that in this solution, the control adjustment cycle is defined as the time from the start of the current active power setpoint adjustment to the start of the next active power setpoint adjustment. For example, the first control adjustment cycle is the time from the start of adjustment at the initial value of 1.00 pu, the end of stable operation, to the start of the next adjustment at 0.80 pu. The adjustment time is defined as the time from the start of adjustment to the end of stable operation. For example, the adjustment time of the first control adjustment cycle is the time from the start of adjustment at the initial value of 1.00 pu to the end of stable operation.

[0032] S32. For the reactive power setting value control dynamic response characteristic simulation working condition, the verification phase model simulation is performed as follows: Two adjacent control adjustment cycles are set; in the first control adjustment cycle, the joint simulation model is set to operate in a capacitive reactive mode, and the reactive setting value is set to a first preset proportion of the reactive output capacity; in the second control adjustment cycle, the joint simulation model is set to operate in an inductive reactive mode, and the reactive setting value is set to the first preset proportion of the reactive output capacity; in both the first and second control adjustment cycles, the active power is maintained within the range of the maximum power generation capacity of the wind turbine; and the model simulation data of the wind turbine under the measured wind speed conditions in the first and second control adjustment cycles are recorded.

[0033] For example, the first preset ratio is set to 0.8pu; that is, the reactive power is set to 0.8pu capacitive reactive mode operation and 0.8pu inductive reactive mode operation in sequence, and the active power is maintained within the range of the maximum power generation that the wind turbine can achieve during the simulation.

[0034] S4. Determine the simulation deviation result of the joint simulation model relative to the actual wind turbine generator system based on the model simulation data and the test data; adjust the model parameters of the joint simulation model according to the simulation deviation result until the simulation deviation result meets the preset deviation threshold, thereby obtaining a general model for power control characteristic simulation.

[0035] Specifically, based on the model simulation data and the test data, the simulation deviation results of each control adjustment cycle of the joint simulation model are determined, including: S41, determining the start time of a power step change, the time when the power first reaches a preset ratio of a set value from an initial value, and the time when the power begins to continuously remain within a preset stable error band based on the power data and the simulation data sequence value; determining the adjustment time of each control adjustment cycle based on the start time of the power step change and the time when the power begins to continuously remain within the preset stable error band; S42, calculating the mean absolute deviation between the simulation value and the test data within the adjustment time according to the start and end times of the kth adjustment time and the response values of the active power and reactive power within the adjustment time; S43. Determine a stable operation time period based on the end time of the mth adjustment time and the start time of the (m+1)th adjustment time, and calculate the mean absolute deviation between the simulation value and the test data in the stable operation time period based on the end time of the mth adjustment time and the start time of the (m+1)th adjustment time, as well as the response values of the active power and the reactive power in the stable operation time period; S44, calculating the response time of the simulation value and the test data based on the power step change start time and the power first reaching a preset ratio of the set value from the initial value; calculating the absolute deviation and relative deviation of the response time between the simulation value and the test data based on the response time; S45 , calculating an overshoot between the simulation value and the test data according to the maximum power response value and the set value, and calculating an overshoot deviation between the simulation value and the test data according to the overshoot.

[0036] More specifically, after the simulation is completed, the model simulation data and test data are compared to calculate the mean absolute deviation within the active power or reactive power control adjustment time. X MAE3 , the average absolute deviation from the time when active or reactive power control adjustment time ends and stabilizes to the time when the next control instruction occurs X MAE4 , absolute deviation of response time ΔT resp1 , relative deviation of response time X tresp1 , overshoot deviation Δσ .

[0037] It should be noted that the deviation calculation involves the calculation of response time, adjustment time, and overshoot. When the power response time in the test data is less than the response time limit T rl When the absolute deviation of the response time between the simulation data and the test data is calculated; when the response time of the power in the test data is greater than or equal to the response time limit T rl When the response time of the model simulation data and the test data is calculated, the relative deviation is calculated.

[0038] The response time is calculated as the time interval from the start of the power step to the moment when the power value first enters the preset error band of the target value.

[0039] The adjustment time is calculated as the interval from the moment the power step change begins to the moment the power begins to remain continuously within the preset stable error band.

[0040] The overshoot is calculated as the difference between the maximum power response value and the set value.

[0041] As an example, the deviation calculation method is as follows: 1) Mean absolute deviation X MAE3 :

[0042] in, t s_start To adjust the data sequence value corresponding to the start time, t s_end To adjust the data sequence value corresponding to the end time, X M ( i ) is the i Active or reactive power test value at a moment, X S ( i ) is the i The simulated value of active or reactive power at a moment.

[0043] 2) Mean absolute deviation X MAE4 :

[0044] in, t next_ins It is the data sequence value corresponding to the start time of the next instruction.

[0045] 3) Absolute deviation of response time ΔT resp1 The calculation method is:

[0046] in, T r_M For test data active power or reactive power response time, T r_S For the simulation data active power or reactive power response time, T rl is the response time limit, when T r_M < T rl , calculate the deviation.

[0047] 4) Relative deviation of response time X tresp1 :

[0048] when T r_M ≥ T rl , calculate the deviation.

[0049] 5) Overshoot deviation Δσ :

[0050] in, σ M For the test data active power or reactive power overshoot, σ S It is the overshoot of active power or reactive power in simulation data.

[0051] The above scheme calculates the various deviation results. If the above deviation results are within the allowable range, it can be considered that the joint simulation model is consistent with the actual operating characteristics of the wind turbine. If the deviation exceeds the allowable range, the model parameters are modified, the simulation is re-performed, and the deviation is calculated until the deviation is within the allowable range.

[0052] S5. Determine an evaluation model simulation model based on the power control characteristic simulation general model and the target model to be evaluated.

[0053] It should be noted that for components that have changed between the evaluation model and the test model, the parameters of the corresponding changed components are modified based on the joint simulation model of the verified test model to obtain the simulation model of the evaluation model.

[0054] Specifically, in the step of determining the evaluation model simulation model based on the power control characteristic simulation general model and the target model to be evaluated, if the components of the target model to be evaluated change, the component parameters in the power control characteristic simulation general model are modified to obtain an evaluation model simulation model consistent with the target model.

[0055] S6. Perform evaluation phase simulation on the evaluation model simulation model to obtain evaluation simulation data; determine the power control characteristic evaluation result of the target model based on the evaluation simulation data; wherein, the operating conditions simulated in the evaluation phase include active power setting value control static error simulation condition, active power setting value control dynamic response characteristic simulation condition, reactive power setting value control static error simulation condition, reactive power setting value control dynamic response characteristic simulation condition, maximum inductive reactive power simulation condition, maximum capacitive reactive power simulation condition and reactive power setting value zero simulation condition.

[0056] Specifically, the evaluation phase simulation is performed on the evaluation model to obtain evaluation simulation data, including: S601, controlling the static error according to the active power setting value and simulating the working condition test to obtain the active absolute static error; controlling the dynamic response characteristic according to the active power setting value and simulating the working condition test to obtain the active power stabilization time, fall time and response time; Specifically, the active power absolute static error is obtained by controlling the static error simulation working condition test according to the active power setting value, including: The simulation conditions of the active power set value control static error simulation conditions are the same as those in the verification stage. After obtaining the simulation data, calculate the active power average value of the simulation data in each control adjustment cycle P S_ave And the maximum absolute static error between the active power simulation value and the set value P static_error . It can be calculated by the following formula:

[0057] in, P S ( i ) is the active power, t con_start and t con_end It is the simulation data sequence value corresponding to the start and end time of each control adjustment cycle.

[0058]

[0059] in, P set Active power absolute static error P static_error The maximum allowable value of the absolute static error of active power should be P static_error_mav Inside.

[0060] Specifically, the active power stabilization time, fall time and response time are obtained by simulating the working condition test based on the active power setting value control dynamic response characteristics, including: The active power set value controls the dynamic response characteristics simulation working condition. The working condition is set with a minimum step size of 0.4pu rated active power to evaluate the dynamic performance of the wind turbine. After obtaining the simulation data, the active power stabilization time, decline time, and response time need to be calculated. The calculation of the stabilization time is the same as the above-mentioned adjustment time calculation method, and the response time calculation method is the above-mentioned response time calculation method. The decline time is the time interval for the measurement signal to drop from 90% to 10% of the target value. The active power stabilization time, decline time, and response time must all be within the maximum allowable values, and the corresponding maximum allowable values are P dynamic_sett_time_mav 、 P dynamic_rampdown_time_mav 、 P dynamic_rep_time_mav .

[0061] S602. According to the working condition tests of the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition and the reactive power setting value zero simulation condition, the average active power, average reactive power, power factor and power factor deviation corresponding to each working condition test are obtained.

[0062] Specifically, the simulation conditions for maximum inductive reactive power, maximum capacitive reactive power, and reactive power zero are similar, and are all simulated in 11 power intervals: -0.05~0.05pu, 0.05~0.15pu, 0.15~0.25pu, 0.25~0.35pu, 0.35~0.45pu, 0.45~0.55pu, 0.55~0.65pu, 0.65~0.75pu, 0.75~0.85pu, 0.85~0.95pu, and 0.95~1.05pu. The difference between the three conditions lies in the setting of the power factor. Let φ ind 、 φ cap 、 φ zero They are the power factor setting values for the maximum inductive reactive power, the maximum capacitive reactive power and the reactive power zero condition. φ ind Set to -0.95, φ cap Set to 0.95, φ zero Set to 1.

[0063] After obtaining the simulation data of the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition, and the reactive power setting value of zero simulation condition, the average active power of each interval is calculated with 60s as an interval. P S_ave , average reactive power Q S_ave , power factor φ and power factor deviation φ error Power Factor φ The power factor deviation should be within the maximum allowable value φ error_mav The calculation method is as follows:

[0064] in, P S is the active power simulation value, t start_60 and t end_60 is the simulation data sequence value corresponding to the start time and end time of each 60s data interval.

[0065]

[0066] in, Q S is the active power simulation value.

[0067]

[0068]

[0069] in, φ is the simulation data power factor, φ set is the power factor setting value, φ error is the power factor deviation.

[0070] S603, controlling the static error according to the reactive power setting value and simulating the working condition test to obtain the reactive absolute static error; controlling the dynamic response characteristic according to the reactive power setting value and simulating the working condition test to obtain the reactive power stabilization time, rise time and response time.

[0071] Specifically, the reactive static simulation is performed in 0.8 pu inductive reactive mode, 0.5 pu inductive reactive mode, reactive 0 mode, 0.5 pu capacitive reactive mode and 0.8 pu capacitive reactive mode. After obtaining the simulation data, the average reactive power of the simulation data in each control adjustment cycle needs to be calculated. QS_ave , the maximum absolute static error between the reactive power simulation value and the set value Q static_error And the terminal voltage U phase . It can be calculated by the following formula:

[0072] in, Q S ( i ) is the active power simulation value.

[0073]

[0074] in, Q set Reactive power absolute static error Q static_error The maximum allowable value of the absolute static error of reactive power should be Q static_error_mav Inside.

[0075]

[0076] in, U RMS is the effective value of voltage.

[0077] The reactive power capability of the model is set to 0.8pu capacitive reactive mode and 0.8pu inductive reactive mode respectively. During the simulation, the active power is maintained within the maximum power generation range that the wind turbine can achieve. After obtaining the simulation data, the reactive power stabilization time, rise time, and response time need to be calculated. The calculation method is the same as the calculation method of the characteristic time in the active dynamic simulation. The rise time is the time interval for the measured signal to rise from 10% to 90% of the target value. The reactive power stabilization time, rise time, and response time must all be within the maximum allowable values, which are Q dynamic_sett_time_mav 、 Q dynamic_rise_time_mav 、 Q dynamic_rep_time_mav .

[0078] Specifically, determining the power control characteristic evaluation result of the target model based on the evaluation simulation data includes: S611, determining an evaluation threshold for each evaluation simulation data; S612: Compare each evaluation simulation data with the corresponding evaluation threshold value. When any evaluation simulation data does not meet the evaluation threshold value, determine that the power control characteristic evaluation result of the target model is unqualified.

[0079] This invention provides a simple, easy-to-use evaluation method for wind turbine power control performance. It can assess the accuracy of wind turbine power control models and effectively evaluate the power control characteristics of related models. This significantly reduces the pressure caused by the high demand for wind turbine power control characteristic evaluation and promotes the rapid development of the wind power industry.

[0080] To further explain and illustrate the method of the present invention, a preferred embodiment of a specific application is provided below. (1) A joint simulation model was constructed based on a wind turbine that had completed a power control field test. The test model had a capacity of 5.5MW.

[0081] (2) Simulate the active static and reactive dynamic conditions. In the active static condition, the active power setting value starts from 1.00 pu and decreases to 0.20 pu in steps of 0.20 pu. Each step lasts for 120 s, and the total simulation time is 600 s. In the reactive dynamic condition, the model reactive power capacity is set to 0.8 pu capacitive reactive mode and 0.8 pu inductive reactive mode respectively. Both operating modes last for 120 s, and the simulation before and after are 60 s, and the total simulation time is 360 s.

[0082] (3) Compare the active and reactive power simulation data with the measured active and reactive power data, and calculate the mean absolute deviation within the control adjustment time X MAE3 , the average absolute deviation from the time when the control adjustment time ends and the next control instruction occurs X MAE4 , absolute deviation of response time ΔT resp1 , relative deviation of response time X tresp1 , overshoot deviation Δσ The verification results are shown in Table 1-2 and Figure 3-4 The maximum allowable value of the power control characteristic control model verification deviation is shown in Table 3: Table 1 Active power control model calibration deviation

[0083] Table 2 Reactive power control model calibration deviation

[0084] Table 3 Maximum allowable deviation of power control characteristic control model verification

[0085] Comparing the deviation tables 1 and 2 with Table 3, we can see that the active and reactive deviations are all within the allowable range. Therefore, the simulation model has the same power control characteristics as the measured model.

[0086] (4) The capacity and blade length of the evaluation model have changed compared to the test model. The capacity has been reduced from 5.5MWW to 5WM, and the blade length has been increased from 76.5 meters to 97 meters. By modifying the relevant parameters, a co-simulation model of the evaluation model can be obtained. Next, the power control characteristic simulation can be performed.

[0087] (5) Active static characteristics The working condition setting is the same as the active static simulation working condition during verification. There are 5 power intervals, which are reduced from 1 pu to 0.2 pu in steps of 0.2 pu. The static error needs to be calculated. The simulation results are shown in Table 4 and Figure 5 As shown. Maximum allowable value of active absolute static error P static_error_mav ±0.05pu, combined with Table 3 and Figure 5 It can be seen that the active static error of this model is within the allowable value.

[0088] Table 4 Static error simulation results

[0089] (6) Active power dynamic characteristics The step size in the active dynamic working condition setting must meet the minimum 0.4pu rated active power. In the actual simulation, the power is set to decrease from 1pu to 0.5pu. After the simulation is completed, the dynamic response results of the simulation data are calculated. The simulation results are shown in Table 5 and Figure 6 The maximum allowable values of settling time, fall time, and response time are shown. P dynamic_sett_time_mav 、 P dynamic_rampdown_time_mav 、 P dynamic_rep_time_mav They are 10s, 7s and 8s respectively. Figure 6 It can be seen that the active dynamic characteristic time is within the maximum allowable value.

[0090] Table 5 Dynamic response results

[0091] (7) Maximum inductive reactive characteristics The maximum inductive reactive working condition is set to -0.05~0.05pu, 0.05~0.15pu, 0.15~0.25pu, 0.25~0.35pu, 0.35~0.45pu, 0.45~0.55pu, 0.55~0.65pu, 0.65~0.75pu, 0.75~0.85pu, 0.85~0.95pu, 0.95~1.05pu 11 power intervals for simulation, and the power factor is -0.95. The simulation time for each power interval is 300s, with 5 60s data segments. After obtaining the simulation data, the average active power, average reactive power, and power factor need to be calculated. The simulation results are shown in Table 6 and Figure 7 As shown. Maximum allowable value of power factor deviation φ error_mav It is ±0.02. It can be seen from Table 6 that the power factor deviation under this working condition is within the maximum allowable deviation value.

[0092] Table 6 Simulation results of maximum inductive reactive power of wind turbines

[0093] (8) Maximum capacitive reactive power The maximum capacitive reactive power condition is set to -0.05~0.05pu, 0.05~0.15pu, 0.15~0.25pu, 0.25~0.35pu, 0.35~0.45pu, 0.45~0.55pu, 0.55~0.65pu, 0.65~0.75pu, 0.75~0.85pu, 0.85~0.95pu, 0.95~1.05pu 11 power intervals for simulation, and the power factor is 0.95. The simulation time for each power interval is 300s, with 5 60s data segments. After obtaining the simulation data, the average active power, average reactive power, and power factor need to be calculated. The simulation results are shown in Table 7 and Figure 8 As shown. Maximum allowable value of power factor deviation φ error_mav It is ±0.02. It can be seen from Table 7 that the power factor deviation under this working condition is within the maximum allowable value of the deviation.

[0094] Table 7 Simulation results of maximum capacitive reactive power of wind turbines

[0095] (9) Reactive power is 0 The reactive power is set to 0 in 11 power intervals of -0.05~0.05pu, 0.05~0.15pu, 0.15~0.25pu, 0.25~0.35pu, 0.35~0.45pu, 0.45~0.55pu, 0.55~0.65pu, 0.65~0.75pu, 0.75~0.85pu, 0.85~0.95pu, and 0.95~1.05pu for simulation, and the power factor is 1. The simulation time for each power interval is 300s, with 5 60s data segments. After obtaining the simulation data, the average active power, average reactive power, and power factor need to be calculated. The simulation results are shown in Table 8 and Figure 9 As shown. Maximum allowable value of power factor deviation φ error_mav It is ±0.02. It can be seen from Table 8 that the power factor deviation under this working condition is within the maximum allowable value of the deviation.

[0096] Table 8 Simulation results of wind turbine reactive power set to zero

[0097] (10) Reactive static characteristics The reactive power static simulation is performed in 0.8 pu inductive reactive mode, 0.5 pu inductive reactive mode, reactive power 0 mode, 0.5 pu capacitive reactive mode and 0.8 pu capacitive reactive mode. Each control cycle is 120 seconds, and the simulation before and after is 60 seconds, and the total simulation time is 720 seconds. After obtaining the simulation data, the average reactive power of the simulation data in each control adjustment cycle needs to be calculated. Q S_ave , the maximum absolute static error between the reactive power simulation value and the set value X reactive_static And the terminal voltage U phase , the simulation results are shown in Table 9 and Figure 10 As shown. Maximum allowable value of absolute static error of reactive power Q static_error_mav ±0.05pu, combined with Table 9 and Figure 10 It can be seen that the reactive static error of this model is within the allowable value.

[0098] Table 9 Static error simulation results

[0099] (11) Reactive dynamic characteristics The reactive power capability of the model was set to 0.8 pu capacitive reactive mode and 0.8 pu inductive reactive mode, respectively. The two power control cycles were 120 seconds each, and the simulations before and after were 60 seconds each, for a total simulation time of 360 seconds. During the simulation, the active power was maintained within the maximum power generation range that the wind turbine could achieve. After obtaining the simulation data, the reactive power stabilization time, rise time, and response time were calculated. The simulation results are shown in Table 10 and Figure 11 The maximum allowable values of settling time, rise time, and response time are shown. Q dynamic_sett_time_mav 、 Q dynamic_rise_time_mav 、 Q dynamic_rep_time_mav They are 0.15s, 0.08s and 0.1s respectively. Figure 11 It can be seen that the active dynamic characteristic time is within the maximum allowable value.

[0100] Table 10 Dynamic response simulation results

[0101] It is judged that the power control characteristics of the wind turbines in the same series meet the evaluation indicators, and it is verified that the wind turbines have the corresponding power control capabilities.

[0102] Example 2 like Figure 12 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a wind turbine power control performance evaluation device, comprising: The first data acquisition module is used to obtain test data of the actual wind turbine under the measured wind speed; the test data includes the measured wind speed, the actual three-phase current and voltage, and the time series; The first model building module is used to determine the joint simulation model of the wind turbine generator system; a second data acquisition module, configured to input the measured wind speed into the joint simulation model for model simulation in a verification phase, thereby obtaining model simulation data of the wind turbine generator set; wherein the operating conditions of the model simulation in the verification phase include a static error simulation condition of active power setting value control and a dynamic response characteristic simulation condition of reactive power setting value control; the model simulation data include three-phase current and voltage simulation data and corresponding simulation data sequence values, as well as power data determined based on the three-phase current and voltage simulation data, wherein the power data includes active power and reactive power; A model adjustment module is used to determine a simulation deviation result of the joint simulation model relative to the actual wind turbine generator system based on the model simulation data and the test data; adjust the model parameters of the joint simulation model according to the simulation deviation result until the simulation deviation result meets a preset deviation threshold, thereby obtaining a general model for power control characteristic simulation; The second model building module is used to determine the evaluation model simulation model according to the power control characteristic simulation general model and the target model to be evaluated; An evaluation module is used to simulate the evaluation phase of the evaluation model simulation model to obtain evaluation simulation data; and determine the power control characteristic evaluation result of the target model based on the evaluation simulation data; wherein, the operating conditions simulated in the evaluation phase include the active power setting value control static error simulation condition, the active power setting value control dynamic response characteristic simulation condition, the reactive power setting value control static error simulation condition, the reactive power setting value control dynamic response characteristic simulation condition, the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition, and the reactive power setting value zero simulation condition.

[0103] Example 3 like Figure 13 As shown, the present invention also provides an electronic device 100 for implementing a method for evaluating the power control performance of a wind turbine generator set; The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0104] The memory 101 can be used to store a computer program 103 . The processor 102 implements the steps of a wind turbine power control performance evaluation method of Example 1 by running or executing the computer program stored in the memory 101 and calling data stored in the memory 101 .

[0105] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0106] The at least one processor 102 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 using various interfaces and lines.

[0107] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for evaluating the power control performance of a wind turbine generator system. The processor 102 can execute the plurality of instructions to implement: Obtain test data of actual wind turbines under measured wind speeds; the test data includes measured wind speeds, actual three-phase currents and voltages, and time series; Determine the joint simulation model of wind turbines; Inputting the measured wind speed into the joint simulation model for model simulation in the verification phase to obtain model simulation data of the wind turbine generator set; wherein the operating conditions of the model simulation in the verification phase include a static error simulation condition of active power setting value control and a dynamic response characteristic simulation condition of reactive power setting value control; the model simulation data includes three-phase current and voltage simulation data and corresponding simulation data sequence values, as well as power data determined based on the three-phase current and voltage simulation data, the power data including active power and reactive power; Determining a simulation deviation result of the joint simulation model relative to the actual wind turbine generator system based on the model simulation data and the test data; adjusting model parameters of the joint simulation model according to the simulation deviation result until the simulation deviation result meets a preset deviation threshold, thereby obtaining a general model for simulating power control characteristics; Determine the simulation model of the evaluation model based on the general model for simulating power control characteristics and the target model to be evaluated; The evaluation phase simulation is performed on the simulation model of the evaluation model to obtain evaluation simulation data; the power control characteristic evaluation result of the target model is determined based on the evaluation simulation data; wherein, the operating conditions simulated in the evaluation phase include the active power setting value control static error simulation condition, the active power setting value control dynamic response characteristic simulation condition, the reactive power setting value control static error simulation condition, the reactive power setting value control dynamic response characteristic simulation condition, the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition and the reactive power setting value zero simulation condition.

[0108] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0109] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device 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.

[0113] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating the power control performance of a wind turbine generator system, characterized in that: include: Obtain test data of actual wind turbines under measured wind speeds; the test data includes measured wind speeds, actual three-phase currents and voltages, and time series; Determine the joint simulation model of wind turbines; Inputting the measured wind speed into the joint simulation model for model simulation in the verification phase to obtain model simulation data of the wind turbine generator set; wherein the operating conditions of the model simulation in the verification phase include a static error simulation condition of active power setting value control and a dynamic response characteristic simulation condition of reactive power setting value control; the model simulation data includes three-phase current and voltage simulation data and corresponding simulation data time series values, as well as power data determined based on the three-phase current and voltage simulation data, the power data including active power and reactive power; Determining a simulation deviation result of the joint simulation model relative to the actual wind turbine generator system based on the model simulation data and the test data; adjusting model parameters of the joint simulation model according to the simulation deviation result until the simulation deviation result meets a preset deviation threshold, thereby obtaining a general model for simulating power control characteristics; Determine the evaluation model simulation model based on the power control characteristic simulation general model and the target model to be evaluated; The evaluation phase simulation is performed on the simulation model of the evaluation model to obtain evaluation simulation data; the power control characteristic evaluation result of the target model is determined based on the evaluation simulation data; wherein, the operating conditions simulated in the evaluation phase include the active power setting value control static error simulation condition, the active power setting value control dynamic response characteristic simulation condition, the reactive power setting value control static error simulation condition, the reactive power setting value control dynamic response characteristic simulation condition, the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition and the reactive power setting value zero simulation condition.

2. The wind turbine power control performance evaluation method according to claim 1, characterized in that: The measured wind speed is input into the joint simulation model to perform model simulation in the verification phase to obtain model simulation data of the wind turbine, including: For the active power setpoint control static error simulation condition, the verification phase model simulation is performed as follows: Determine the initial value of the active power set value of the joint simulation model; under the measured wind speed condition, reduce the active power set value from the initial value by a preset step size in each control adjustment cycle to the preset target value; record the model simulation data of each control adjustment cycle; For the reactive power setpoint control dynamic response characteristic simulation condition, the verification stage model simulation is performed as follows: Two adjacent control adjustment cycles are set; in the first control adjustment cycle, the joint simulation model is set to operate in a capacitive reactive mode, and the reactive setting value is set to a first preset proportion of the reactive output capacity; in the second control adjustment cycle, the joint simulation model is set to operate in an inductive reactive mode, and the reactive setting value is set to the first preset proportion of the reactive output capacity; in both the first and second control adjustment cycles, the active power is maintained within the range of the maximum power generation capacity of the wind turbine; and the model simulation data of the first and second control adjustment cycles under the measured wind speed conditions are recorded.

3. The wind turbine power control performance evaluation method according to claim 2, characterized in that: Based on the model simulation data and test data, the simulation deviation results of the joint simulation model relative to the actual wind turbine are determined, including: Determine, based on the power data and the simulation data sequence value, the time when the power step change starts, the time when the power first reaches a preset proportion of the set value from the initial value, and the time when the power starts to continuously remain within a preset stable error band; Determine the adjustment time of each control adjustment cycle according to the time when the power step change starts and the time when the power starts to be continuously maintained within the preset stable error band; According to the start and end time of the kth adjustment time and the power data during the adjustment time, the average absolute deviation between the model simulation data and the test data during the adjustment time is calculated; Determine a stable operation time period according to the end time of the mth adjustment time and the start time of the m+1th adjustment time, and calculate the average absolute deviation between the model simulation data and the test data in the stable operation time period according to the end time of the mth adjustment time and the start time of the m+1th adjustment time and the power data in the stable operation time period; Calculate the response time of the power in the model simulation data and the test data based on the start time of the power step change and the time when the power first reaches a preset proportion of the set value from the initial value; when the response time of the power in the test data is less than the response time limit, calculate the absolute deviation of the response time between the model simulation data and the test data; when the response time of the power in the test data is greater than or equal to the response time limit, calculate the relative deviation of the response time between the model simulation data and the test data; The overshoot of the model simulation data and the test data is calculated according to the maximum power response value and the set value, and the overshoot deviation of the model simulation data and the test data is calculated according to the overshoot.

4. The wind turbine power control performance evaluation method according to claim 1, characterized in that: Performing an evaluation phase simulation on the evaluation model simulation model to obtain evaluation simulation data includes: According to the active power setting value, the static error is controlled by simulating the working condition test to obtain the active absolute static error; according to the active power setting value, the dynamic response characteristic is controlled by simulating the working condition test to obtain the active power stabilization time, fall time and response time; According to the working condition tests of the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition and the reactive power setting value zero simulation condition, the average active power, average reactive power, power factor and power factor deviation corresponding to each working condition test are obtained; According to the reactive power setting value, the static error is controlled by simulating the working condition test to obtain the absolute static error of reactive power; according to the reactive power setting value, the dynamic response characteristic is controlled by simulating the working condition test to obtain the reactive power stabilization time, rise time and response time.

5. The wind turbine power control performance evaluation method according to claim 1, characterized in that: In the step of determining the evaluation model simulation model based on the power control characteristic simulation general model and the target model to be evaluated, if the components of the target model to be evaluated change, the component parameters in the power control characteristic simulation general model are modified to obtain the evaluation model simulation model consistent with the target model.

6. The wind turbine power control performance evaluation method according to claim 1, characterized in that: The joint simulation model includes a Bladed mechanical model and a Matlab electrical model.

7. The wind turbine power control performance evaluation method according to claim 1, characterized in that: Determine the power control characteristics evaluation results of the target model based on the evaluation simulation data, including: Determine the evaluation threshold of each evaluation simulation data; Each evaluation simulation data is compared with the corresponding evaluation threshold. When any evaluation simulation data does not meet the evaluation threshold, the power control characteristic evaluation result of the target model is judged to be unqualified.

8. A wind turbine power control performance evaluation device, characterized in that: include: The first data acquisition module is used to obtain test data of the actual wind turbine generator set under the measured wind speed; the test data includes the measured wind speed, the actual three-phase current and voltage, and the time series; The first model building module is used to determine the joint simulation model of the wind turbine generator system; a second data acquisition module, configured to input the measured wind speed into the joint simulation model for model simulation in the verification phase, thereby obtaining model simulation data of the wind turbine generator set; wherein the operating conditions of the model simulation in the verification phase include a static error simulation condition of active power setting value control and a dynamic response characteristic simulation condition of reactive power setting value control; the model simulation data include three-phase current and voltage simulation data and corresponding simulation data time series values, as well as power data determined based on the three-phase current and voltage simulation data, wherein the power data includes active power and reactive power; A model adjustment module is used to determine a simulation deviation result of the joint simulation model relative to the actual wind turbine generator system based on the model simulation data and the test data; adjust the model parameters of the joint simulation model according to the simulation deviation result until the simulation deviation result meets a preset deviation threshold, thereby obtaining a general model for power control characteristic simulation; The second model building module is used to determine the evaluation model simulation model according to the power control characteristic simulation general model and the target model to be evaluated; An evaluation module is used to simulate the evaluation phase of the evaluation model simulation model to obtain evaluation simulation data; and determine the power control characteristic evaluation result of the target model based on the evaluation simulation data; wherein, the operating conditions simulated in the evaluation phase include the active power setting value control static error simulation condition, the active power setting value control dynamic response characteristic simulation condition, the reactive power setting value control static error simulation condition, the reactive power setting value control dynamic response characteristic simulation condition, the maximum inductive reactive power simulation condition, the maximum capacitive reactive power simulation condition, and the reactive power setting value zero simulation condition.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the method for evaluating the power control performance of a wind turbine generator set according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the method for evaluating the power control performance of a wind turbine generator system according to any one of claims 1 to 7 is implemented.

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