Accuracy evaluation method and system of wind power plant equivalent model, equipment and medium

Abstract

The invention provides an accuracy evaluation method and system for a wind power plant equivalent model, equipment and a medium, and relates to the technical field of power system simulation modeling. The method comprises the following steps: simulating different operation scenes based on a wind power plant equivalent model, and extracting equivalent electrical quantity curves and machine end equivalent impedance curves of a plurality of feature dimensions; carrying out power flow consistency evaluation based on error statistics between the equivalent electric quantity curve and the reference electric quantity curve; if the plurality of feature dimensions pass the power flow consistency evaluation, performing impedance consistency evaluation based on the difference between the machine end equivalent impedance curve and the reference impedance curve; or performing parameter sensitive consistency evaluation based on the similarity between the key control parameter and the corresponding reference control parameter; and determining the accuracy of the wind power plant equivalent model based on an impedance consistency evaluation result or a parameter sensitivity consistency evaluation result. According to the method, the problems that an existing accuracy evaluation mode of the wind power plant equivalent model is low in reliability and lacks a guiding effect on modeling are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system simulation modeling, in particular to a wind farm equivalent model accuracy evaluation method and system, equipment and medium. BACKGROUND

[0002] With the continuous deepening of wind energy development and utilization, large-scale wind power access to the power system causes a series of complex difficulties in the modeling and simulation of the power system. Large-scale wind power bases usually contain numerous wind farms and a large number of wind turbines. Although the wind farm detailed model with reserved internal topology has high simulation accuracy, it will lead to long calculation time, difficult power flow convergence, excessive calculation scale, power system information security and other problems. In the face of these problems, the detailed model is simplified by developing a wind farm equivalent model, and the simulation accuracy of the wind farm equivalent model becomes a key problem.

[0003] At present, the qualitative evaluation method based on visual inspection is generally used to verify the accuracy of the wind farm equivalent model, and this method depends on subjectivity, and the reliability of the evaluation result is not high and lacks guidance for modeling. SUMMARY

[0004] In order to overcome the problems of low reliability of the wind farm equivalent model accuracy evaluation method and lack of guidance for modeling, the present application provides a wind farm equivalent model accuracy evaluation method and system, equipment and medium.

[0005] In one aspect, the present application provides a wind farm equivalent model accuracy evaluation method, comprising:

[0006] Based on the wind farm equivalent model, different operating scenarios of the target wind farm are simulated, and equivalent electrical quantity curves of multiple feature dimensions and terminal equivalent impedance curves of simulation output are extracted respectively;

[0007] For each feature dimension, the power flow consistency of the wind farm equivalent model is evaluated based on the error statistics between the equivalent electrical quantity curve of the feature dimension and the corresponding reference electrical quantity curve;

[0008] If multiple feature dimensions pass the power flow consistency evaluation, the impedance consistency of the wind farm equivalent model is evaluated based on the difference between the terminal equivalent impedance curve and the reference impedance curve; or, the parameter sensitivity consistency of the wind farm equivalent model is evaluated based on the similarity between the key control parameters of the target feature dimension and the corresponding reference control parameters;

[0009] The accuracy of the wind farm equivalent model is determined based on the impedance consistency evaluation result or the parameter sensitivity consistency evaluation result;

[0010] The reference electrical quantity curve and the reference impedance curve are respectively used to represent standard output electrical quantity curves of the target wind farm in different operation scenarios; the reference control parameter is used to represent a control parameter affecting the standard output electrical quantity curves of the target wind farm, and the key control parameter is a control parameter affecting the output electrical quantity of the wind farm equivalent model; and the target feature dimension is at least one of a plurality of feature dimensions.

[0011] Optionally, the plurality of feature dimensions include active power, reactive power, output voltage, output current, and reactive current.

[0012] Optionally, before the error statistics between the equivalent electrical quantity curve based on the feature dimension and the corresponding reference electrical quantity curve are used to perform the power flow consistency evaluation of the wind farm equivalent model, the method further includes:

[0013] Multiple simulations of the target wind farm in different operation scenarios are performed using the wind farm equivalent model, and multiple sets of equivalent electrical quantity curves with multiple feature dimensions are correspondingly obtained;

[0014] For each feature dimension, a corresponding error sample set is generated based on the relative average error between the multiple sets of equivalent electrical quantity curves and the corresponding reference electrical quantity curve at each sampling point;

[0015] A confidence interval of the error sample set corresponding to each feature dimension is calculated based on a statistical method and a preset significance level;

[0016] An error threshold of the wind farm equivalent model in each feature dimension is determined based on the confidence interval of the error sample set corresponding to each feature dimension;

[0017] The error statistics between the equivalent electrical quantity curve based on the feature dimension and the corresponding reference electrical quantity curve are used to perform the power flow consistency evaluation of the wind farm equivalent model, including:

[0018] If the relative average error between the equivalent electrical quantity curve of the feature dimension and the corresponding reference electrical quantity curve is less than or equal to the corresponding error threshold, it is determined that the wind farm equivalent model passes the power flow consistency evaluation in the current feature dimension.

[0019] Optionally, the error threshold of the wind farm equivalent model in each feature dimension is as follows:

[0020]

[0021] wherein ε m * εm is the error threshold of the wind farm equivalent model in the mth feature dimension, εm is the upper limit of the confidence interval of the wind farm equivalent model in the mth feature dimension, is the average value of the error sample set corresponding to the mth feature dimension, and a is a confidence level, is the critical value of the t-distribution with degrees of freedom M-1 at the confidence level a, and S m is the standard deviation of the error sample set corresponding to the mth feature dimension, and M m is the number of samples of the error sample set corresponding to the mth feature dimension, and ε i,m is the ith error sample in the error sample set corresponding to the mth feature dimension.

[0022] Optionally, the impedance consistency evaluation of the wind farm equivalent model is performed based on the difference between the machine-side equivalent impedance curve and the reference impedance curve, and the impedance consistency evaluation of the wind farm equivalent model comprises:

[0023] Based on the difference between the machine-side equivalent impedance curve and the reference impedance curve at each frequency, an impedance evaluation index of the wind farm equivalent model is determined.

[0024] If the impedance evaluation index is less than or equal to an impedance threshold value, it is determined that the wind farm equivalent model passes the impedance consistency evaluation.

[0025] Optionally, before the parameter sensitivity consistency evaluation of the wind farm equivalent model is performed based on the similarity between the target feature dimension-based key control parameter and the corresponding reference control parameter, the method further comprises:

[0026] For each target feature dimension, a plurality of candidate control parameters, a plurality of candidate control parameter combination samples are generated by using a quasi-Monte Carlo method;

[0027] Based on each combination sample, the wind farm equivalent model is run, and the electrical quantity curve of each target feature dimension output by the wind farm equivalent model is extracted;

[0028] For each target feature dimension, based on the Sobol analysis method and the electrical quantity curve of the target feature dimension, the sensitivity of each candidate control parameter is analyzed, and based on the sensitivity analysis result, the key control parameter of the target feature dimension is selected from the plurality of candidate control parameters.

[0029] The reference control parameter is determined based on the wind farm detailed model corresponding to the wind farm equivalent model.

[0030] Optionally, the target feature dimension includes active power and reactive power.

[0031] The plurality of candidate control parameters of the active power include a variable pitch proportional gain, a maximum power point tracking time constant, and a current loop integral time in the wind turbine generator model.

[0032] The multiple candidate control parameters of the reactive power include a voltage control loop proportional coefficient, a voltage control loop integral coefficient, a reactive current loop control coefficient, and a reactive-voltage droop coefficient.

[0033] Optionally, the similarity between the key control parameter of the target feature dimension and the corresponding reference control parameter is used to perform parameter sensitivity consistency evaluation on the wind farm equivalent model, including:

[0034] For each target feature dimension, the similarity between the key control parameter of the target feature dimension and the corresponding reference control parameter is calculated.

[0035] If the similarity between the key control parameter of the target feature dimension and the corresponding reference control parameter is greater than or equal to a sensitivity threshold, it is determined that the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model in the target feature dimension is consistent.

[0036] If the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model in each target feature dimension is consistent, it is determined that the wind farm equivalent model passes the parameter sensitivity consistency evaluation.

[0037] Optionally, the operating scenarios include transient scenarios and wide frequency oscillation scenarios; and the accuracy of the wind farm equivalent model is determined based on the impedance consistency evaluation result or the parameter sensitivity consistency evaluation result, including:

[0038] For transient scenarios, the accuracy of the wind farm equivalent model is determined based on the parameter sensitivity consistency evaluation result.

[0039] For wide frequency oscillation scenarios, the accuracy of the wind farm equivalent model is determined based on the impedance consistency evaluation result.

[0040] Optionally, the operating scenarios include steady-state scenarios; and after the error statistics between the equivalent electrical quantity curve of the feature dimension and the corresponding reference electrical quantity curve are used to perform power flow consistency evaluation on the wind farm equivalent model, the method further includes:

[0041] For steady-state scenarios, the accuracy of the wind farm equivalent model is determined based on the power flow consistency evaluation result of the multiple feature dimensions.

[0042] In another aspect, the application also provides a wind farm equivalent model accuracy evaluation method system, including:

[0043] A multi-dimensional feature extraction module is configured to simulate different operating scenarios of a target wind farm based on a wind farm equivalent model, and extract equivalent electrical quantity curves and terminal equivalent impedance curves of multiple feature dimensions of simulation outputs, respectively.

[0044] a power flow evaluation module, configured to perform power flow consistency evaluation of the wind farm equivalent model based on error statistics between the equivalent electrical quantity curve of each characteristic dimension and the corresponding reference electrical quantity curve;

[0045] a characteristic evaluation module, configured to perform impedance consistency evaluation of the wind farm equivalent model based on differences between the equivalent impedance curve and the reference impedance curve if all the characteristic dimensions pass the power flow consistency evaluation, or perform parameter sensitivity consistency evaluation of the wind farm equivalent model based on similarities between the key control parameter of the target characteristic dimension and the corresponding reference control parameter;

[0046] an accuracy evaluation module, configured to determine accuracy of the wind farm equivalent model based on the impedance consistency evaluation result or the parameter sensitivity consistency evaluation result;

[0047] wherein the reference electrical quantity curve and the reference impedance curve are respectively used to represent standard output electrical quantity curves of the target wind farm in different operating scenarios; the reference control parameter is used to represent a control parameter affecting the standard output electrical quantity curves of the target wind farm, and the key control parameter is a control parameter affecting output electrical quantity of the wind farm equivalent model; and the target characteristic dimension is at least one of the multiple characteristic dimensions.

[0048] Optionally, the multiple characteristic dimensions include active power, reactive power, output voltage, output current and reactive current.

[0049] Optionally, the power flow evaluation module comprises an error threshold quantification submodule, which is configured to:

[0050] perform multiple simulations of the target wind farm in different operating scenarios by using the wind farm equivalent model, and correspondingly obtain multiple sets of equivalent electrical quantity curves with multiple characteristic dimensions;

[0051] for each characteristic dimension, generate a corresponding error sample set based on relative average errors between sampling points on the multiple sets of equivalent electrical quantity curves of the characteristic dimension and the corresponding reference electrical quantity curve;

[0052] calculate a confidence interval of the error sample set corresponding to each characteristic dimension based on a statistical method and a preset significance level;

[0053] determine an error threshold of the wind farm equivalent model in each characteristic dimension based on the confidence interval of the error sample set corresponding to each characteristic dimension;

[0054] the power flow evaluation module further comprises:

[0055] a power flow evaluation submodule, configured to determine that the wind farm equivalent model passes the power flow consistency evaluation in the current characteristic dimension if a relative average error between the equivalent electrical quantity curve of the characteristic dimension and the corresponding reference electrical quantity curve is less than or equal to a corresponding error threshold.

[0056] Optionally, the error threshold of the wind farm equivalent model in each characteristic dimension is as follows:

[0057]

[0058] wherein ε m * εm is the error threshold of the wind farm equivalent model in the mth characteristic dimension, εm+ is the upper limit of the confidence interval of the wind farm equivalent model in the mth characteristic dimension, is the average value of the error sample set corresponding to the mth characteristic dimension, and a is a confidence level, is the critical value of the t-distribution with M-1 degrees of freedom at the confidence level a, and S m is the standard deviation of the error sample set corresponding to the mth characteristic dimension, M m is the sample quantity of the error sample set corresponding to the mth characteristic dimension, and ε i,m is the ith error sample in the error sample set corresponding to the mth characteristic dimension.

[0059] Optionally, the characteristic evaluation module comprises an impedance evaluation submodule, which is configured to:

[0060] determine an impedance evaluation index of the wind farm equivalent model based on the difference between the machine terminal equivalent impedance curve and the reference impedance curve at each frequency;

[0061] if the impedance evaluation index is less than or equal to an impedance threshold, determine that the wind farm equivalent model passes the impedance consistency evaluation.

[0062] Optionally, the method further comprises a key parameter screening module, which is configured to:

[0063] for each target characteristic dimension, generate a plurality of groups of combined samples of candidate control parameters using a quasi-Monte Carlo method;

[0064] run the wind farm equivalent model based on each group of combined samples, and extract electrical quantity curves of each target characteristic dimension output by the wind farm equivalent model;

[0065] for each target characteristic dimension, perform a sensitivity analysis on each candidate control parameter based on a Sobol analysis method and the electrical quantity curve of the target characteristic dimension, and screen a key control parameter of the target characteristic dimension from the plurality of candidate control parameters based on the sensitivity analysis result.

[0066] The reference control parameter is determined based on running of each set of combined sample and a wind farm detailed model corresponding to the wind farm equivalent model.

[0067] Optionally, the target feature dimension includes active power and reactive power.

[0068] The multiple candidate control parameters of the active power include a pitch proportional gain, a maximum power point tracking time constant and a current loop integral time in a wind turbine generator model.

[0069] The multiple candidate control parameters of the reactive power include a voltage control loop proportional coefficient, a voltage control loop integral coefficient, a reactive current loop control coefficient and a reactive-voltage droop coefficient.

[0070] Optionally, the characteristic evaluation module includes a parameter sensitivity evaluation submodule, which is configured to:

[0071] For each target feature dimension, a similarity between the key control parameter of the target feature dimension and the corresponding reference control parameter is calculated.

[0072] If the similarity between the key control parameter of the target feature dimension and the corresponding reference control parameter is greater than or equal to a sensitivity threshold, it is determined that the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model in the target feature dimension is consistent.

[0073] If the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model in each target feature dimension is consistent, it is determined that the wind farm equivalent model passes the parameter sensitivity consistency evaluation.

[0074] Optionally, the operation scenario includes a transient scenario and a wide frequency oscillation scenario; and the accuracy evaluation module is specifically configured to:

[0075] For the transient scenario, the accuracy of the wind farm equivalent model is determined based on the parameter sensitivity consistency evaluation result.

[0076] For the wide frequency oscillation scenario, the accuracy of the wind farm equivalent model is determined based on the impedance consistency evaluation result.

[0077] Optionally, the operation scenario includes a steady state scenario; and the accuracy evaluation module is further configured to:

[0078] For the steady state scenario, the accuracy of the wind farm equivalent model is determined based on the power flow consistency evaluation result of the multiple feature dimensions.

[0079] In another aspect, the present application further provides an electronic device, including at least one processor and a memory; the memory and the processor are connected through a bus.

[0080] the memory, configured to store one or more programs;

[0081] the one or more programs, when executed by the at least one processor, implement the method according to any one of the preceding method embodiments.

[0082] In another aspect, the present application also provides a readable storage medium having an execution program stored thereon, the execution program, when executed, implements the method according to any one of the preceding method embodiments.

[0083] Compared with the prior art, the present application has the following beneficial effects:

[0084] The present application provides a wind farm equivalent model accuracy evaluation method and system, which extracts multiple characteristic dimension equivalent electrical quantity curves to perform power flow consistency evaluation of multiple characteristic dimension wind farm equivalent models, thereby improving the reliability of model power flow consistency evaluation; by increasing impedance consistency evaluation, the problem of oscillation stability misjudgment of the simulation results of the wind farm equivalent model caused by mismatch of terminal impedance characteristics in the key frequency band is avoided; by performing parameter sensitivity consistency evaluation on the wind farm equivalent model between the similarity of the target characteristic dimension key control parameters and the corresponding reference control parameters, by increasing the parameter sensitivity consistency evaluation, the multi-level control system coupling effect of the dynamic characteristics of the key characteristic dimensions of the wind farm is considered, and the time sequence consistency of the dynamic response of the wind farm equivalent model is ensured. The present application improves the reliability of the accuracy evaluation of the wind farm equivalent model by setting multiple evaluation processes, and at the same time, the evaluation results of each layer can provide certain guidance for the accurate construction of the wind farm equivalent model. BRIEF DESCRIPTION OF DRAWINGS

[0085] Figure 1 It is a flowchart of the wind farm equivalent model accuracy evaluation method of the present application;

[0086] Figure 2 It is a three-phase current waveform diagram of an example set of wind farm export grid connection points of the present application;

[0087] Figure 3 It is an active power waveform diagram of an example set of wind farm export grid connection points of the present application;

[0088] Figure 4 It is a reactive power waveform diagram of an example set of wind farm export grid connection points of the present application;

[0089] Figure 5 It is a three-phase voltage waveform diagram of an example set of wind farm export grid connection points of the present application;

[0090] Figure 6A wind farm detailed model and a machine terminal impedance diagram of a wind farm equivalent model of an example of the present application;

[0091] Figure 7 An electronic device structure block diagram of the present application. DETAILED DESCRIPTION

[0092] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0093] Example 1

[0094] The present application provides a wind farm equivalent model accuracy evaluation method, and a schematic diagram thereof is shown in FIG. 1. Figure 1 The method comprises the following steps:

[0095] In step S110, different operating scenarios of a target wind farm are simulated based on a wind farm equivalent model, and equivalent electrical quantity curves and machine terminal equivalent impedance curves of multiple characteristic dimensions of simulation output are extracted respectively.

[0096] In step S120, for each characteristic dimension, a power flow consistency evaluation of the wind farm equivalent model is performed based on an error statistic quantity between the equivalent electrical quantity curve of the characteristic dimension and a corresponding reference electrical quantity curve.

[0097] In step S130, if the multiple characteristic dimensions all pass the power flow consistency evaluation, an impedance consistency evaluation of the wind farm equivalent model is performed based on a difference between the machine terminal equivalent impedance curve and a reference impedance curve; or a parameter sensitivity consistency evaluation of the wind farm equivalent model is performed based on a similarity between a target characteristic dimension key control parameter and a corresponding reference control parameter.

[0098] In step S140, an accuracy of the wind farm equivalent model is determined based on an impedance consistency evaluation result or a parameter sensitivity consistency evaluation result.

[0099] In the example embodiment, the characteristic dimension refers to various types of electrical quantity characteristic dimensions of the target wind farm output, i.e., various types of electrical quantities at the grid connection point of the target wind farm. For example, the wind farm equivalent model needs to meet the grid connection requirements and maintain high fidelity for key electrical quantities. The model evaluation index for actual engineering should have practical significance and be able to guide engineering applications. Therefore, when selecting characteristic electrical quantity curves based on model evaluation, it should be consistent with the grid connection requirements and easy to measure and compare in actual scenarios. In this example, in combination with the technical regulations for connecting wind farms to power systems, the selected multiple characteristic dimensions can include active power, reactive power, output voltage, output current, and reactive current. Different operating scenarios can include steady-state and non-steady-state operating scenarios of the wind farm. For example, the operating scenarios of the target wind farm include steady-state scenarios, transient scenarios, and wide-frequency oscillation scenarios. The steady-state scenario refers to the continuous stable operation stage of the wind farm. The transient scenario refers to the transition process from the initial state to the new steady state or stable state before the power system reaches a new steady state or stable state after being disturbed, for example, the process from voltage drop to stability caused by power grid failure. The electrical quantity time window in the transient scenario can be divided into the fault period and the recovery period. The characteristic primary electrical quantity in the transient state will drop after the fault. The low voltage ride through strategy works when the voltage drops, and the reactive current is increased to support the voltage. Then it enters the recovery period and recovers to the steady state. Therefore, the change law of the transient process of the wind power grid-connected system can be reflected by the main characteristic points, and determining these characteristic points is of great significance to accurately evaluate the index of each stage of the transient state. Therefore, the characteristic point selection of the transient characteristic electrical quantity curve includes the voltage drop minimum point and the recovery maximum point. The wind farm needs to have fault ride-through capability during power grid failure, and it can quickly recover after voltage drop. The disturbance period and the recovery period focus on these two points respectively. The characteristic point selection of the transient characteristic electrical quantity curve also includes the reactive current rise time: when the voltage drops, new energy units need to be able to emit reactive current to support the voltage according to existing standards, so the dynamic reactive current increase time and increment are concerned. In the electrical quantity curve sampling process, sampling the above characteristic points enables the extracted data to represent the change law of the transient process. The wide-frequency oscillation scenario refers to a continuous oscillation scenario (such as subsynchronous oscillation) of the power grid after a small disturbance. The time window of this scenario covers multiple oscillation periods. The non-steady-state operating scenario can include transient and wide-frequency oscillation scenarios. For different non-steady-state operating scenarios, the accuracy of the equivalent model can be further judged through impedance consistency evaluation or parameter sensitivity consistency evaluation. The reference electrical quantity curve and the reference impedance curve are respectively used to represent the standard output electrical quantity curve of the target wind farm in different operating scenarios. The reference electrical quantity curve and the reference impedance curve can be based on the measured data of the wind farm or the simulation results based on the detailed model of the wind farm. The reference control parameter is used to represent the control parameter that affects the standard output electrical quantity curve of the target wind farm. The reference control parameter can be determined based on the detailed model of the wind farm, for example, the reference control parameter is the control parameter that affects the output electrical quantity of the detailed model of the wind farm.The key control parameter is a control parameter affecting the output electrical quantity of the wind farm equivalent model; the target feature dimension is at least one of multiple feature dimensions, which can be determined according to actual needs. The application takes the simulation results or actual operation data of the detailed model of the wind farm as the benchmark to evaluate the wind farm equivalent model in multiple scenes, multiple dimensions and multiple levels.

[0100] Exemplarily, each link of the wind power system and the external power system has a multi-time scale control characteristic, and after mutual coupling, the transient process of the entire wind power grid-connected system has multiple time window periods. For example, when the grid voltage suddenly rises, the fault ride-through response needs to be completed in a short time, and after the fault is removed, there is a long recovery process. Different time window system characteristics are different, so the multi-time scale characteristics need to be considered, and the accuracy evaluation can be divided into time windows. When the simulation results of the detailed model of the wind farm are taken as the benchmark, the model can be run by sequentially simulating different operation scenes, so that the electrical quantity curves of different scenes are located in different time windows, and the evaluation in different time windows can realize the evaluation in multiple scenes. The application improves the time domain accuracy by splitting the operation scene into steady state, transient state and wide frequency oscillation, and combining with the voltage drop point, reactive current increment and other feature point analysis. The application fills the gap in dynamic response and parameter coupling analysis of the existing evaluation system by fusing multiple feature dimension electrical quantities, impedance characteristics and parameter sensitivity analysis, covering wide frequency oscillation and multiple time scale scenes.

[0101] In some example embodiments, before the error statistics between the equivalent electrical quantity curve of the feature dimension and the corresponding benchmark electrical quantity curve are used to evaluate the power flow consistency of the wind farm equivalent model in S120, it further includes:

[0102] Multiple simulations of different operation scenes of the target wind farm are performed using the wind farm equivalent model, and multiple sets of equivalent electrical quantity curves with multiple feature dimensions are obtained correspondingly;

[0103] For each feature dimension, a corresponding error sample set is generated based on the relative average error between the multiple sets of equivalent electrical quantity curves and the corresponding benchmark electrical quantity curve at each sampling point;

[0104] The confidence interval of the error sample set corresponding to each feature dimension is calculated based on a statistical method and a preset significance level;

[0105] Based on the confidence interval of the error sample set corresponding to each feature dimension, the error threshold of the wind farm equivalent model in each feature dimension is determined;

[0106] The error statistics between the equivalent electrical quantity curve of the feature dimension and the corresponding benchmark electrical quantity curve are used to evaluate the power flow consistency of the wind farm equivalent model in S120, including:

[0107] If the relative average error between the equivalent electrical quantity curve of the characteristic dimension and the corresponding reference electrical quantity curve is less than or equal to the corresponding error threshold, it is determined that the wind farm equivalent model passes the power flow consistency evaluation in the current characteristic dimension.

[0108] In the present example embodiment, multiple simulations under each scenario can be respectively performed by using the wind farm equivalent model and the wind farm detailed model. Exemplarily, under various scenarios, multiple simulations of each model are performed by the Monte Carlo method, and the parameter values can be changed in the multiple simulations. For example, for transient scenarios in which voltage dips occur, multiple simulation experiments of different dip depths can be performed to obtain multiple sets of equivalent electrical quantity curves and corresponding reference curves with multiple characteristic dimensions, wherein each set of data has equivalent / reference electrical quantity curves with multiple characteristic dimensions. Then, the accuracy evaluation index is selected, and each characteristic electrical quantity of the equivalent model and the detailed model in the time window under multiple scenarios is compared, and the relative average error is taken as the quantifiable accuracy evaluation index, and the calculation formula is shown in formula (1):

[0109]

[0110] wherein X eq and X ref are characteristic electrical quantity parameters of the wind farm equivalent model and the wind farm detailed model, N is the total number of samples in the time window corresponding to the scenario, ε is the relative average error of the equivalent model characteristic electrical quantity, and t k is the time corresponding to the kth sample point. By calculating the relative average error between each sample point on the multiple sets of equivalent electrical quantity curves and the corresponding reference electrical quantity curves, the error sample set {ε 1,m ,...,ε i,m ,...,ε M,m} of each characteristic dimension is obtained, and ε M,m is the Mth error sample in the mth characteristic dimension corresponding error sample set, and M is the number of error samples. Assuming that the error samples follow a normal distribution, the confidence interval is constructed by using the mean and standard deviation of the error samples, the mean represents the error center position, and the standard deviation represents the dispersion degree. Exemplarily, the error threshold of the wind farm equivalent model in each characteristic dimension is as follows:

[0111]

[0112] wherein ε m * is the error threshold of the wind farm equivalent model in the mth characteristic dimension, is the upper limit of the confidence interval of the wind farm equivalent model in the mth characteristic dimension, is the average value of the error sample set corresponding to the mth feature dimension, and a is the confidence level, which can be 95% confidence level commonly used, is the critical value of t-distribution with M-1 degrees of freedom at the confidence level a, and S m is the standard deviation of the error sample set corresponding to the mth feature dimension, and M m is the sample number of the error sample set corresponding to the mth feature dimension, and e i,m is the ith error sample in the error sample set corresponding to the mth feature dimension. In the example, the confidence level and the confidence interval are introduced for the determination of the error threshold. The confidence interval is a statistical method that can give a probability range containing the true error value. By introducing the confidence interval, the model error can be more accurately and objectively evaluated. In the example, the error confidence interval (95% confidence level) is calculated based on the normal distribution assumption, and the upper limit of the interval is rounded as the error threshold (e.g., 3.1% is rounded to 4%), which replaces the empirical threshold setting. After obtaining the error threshold of a feature dimension, if the relative average error between the equivalent electrical quantity curve of the feature dimension and the reference electrical quantity curve is less than or equal to the corresponding error threshold, it is determined that the wind farm equivalent model passes the power flow consistency evaluation in the current feature dimension, i.e., the wind farm equivalent model has power flow consistency in the current feature dimension.

[0113] In some embodiments, the impedance consistency evaluation of the wind farm equivalent model based on the difference between the machine-side equivalent impedance curve and the reference impedance curve in S130 comprises:

[0114] determining an impedance evaluation index of the wind farm equivalent model based on the difference between the machine-side equivalent impedance curve and the reference impedance curve at each frequency;

[0115] if the impedance evaluation index is less than or equal to an impedance threshold, it is determined that the wind farm equivalent model passes the impedance consistency evaluation.

[0116] In the example embodiment, the transient operating scenario can be simulated by injecting a wideband small perturbation signal (0Hz-1kHz) into the target wind farm grid point, the machine-side impedance curves of the wind farm detailed model and the wind farm equivalent model are extracted, and the difference degree in each frequency band is quantified, as follows:

[0117]

[0118] wherein D impedance is the impedance evaluation index of the wind farm equivalent model, and Z eq (f) respectively represent the impedance of the wind farm equivalent model and the detailed model at frequency f, and f detail (f) respectively represent the impedance of the wind farm equivalent model and the detailed model at frequency f, and f min , f maxrespectively represent the lower and upper bounds of the frequency, the impedance threshold value can be set as 8%-12%, for example, the impedance threshold value is 10%, if D impedence ≤10% is regarded as consistent with the impedance characteristics of the detailed model. In this example, a wide frequency small disturbance is injected into the terminal of the wind farm, the impedance curve is obtained by the sweep frequency method, the impedance difference degree of the specified frequency band (sub-synchronous, medium frequency, high frequency) is calculated, and the difference threshold is set as 10% to determine the impedance characteristic consistency.

[0119] In some embodiments, due to the multiple control links of the wind farm, the number of key control parameters affecting each characteristic dimension can be large and there can be coupling effects, so it is necessary to comprehensively evaluate the influence of each parameter, that is, to perform global sensitivity analysis. Therefore, before the similarity between the key control parameter of the target characteristic dimension and the corresponding reference control parameter is used to perform parameter sensitivity consistency evaluation on the wind farm equivalent model, it further includes:

[0120] For each target characteristic dimension, a quasi-Monte Carlo method is used to generate a plurality of combined samples of candidate control parameters;

[0121] Based on each combined sample, the wind farm equivalent model is run, and the electrical quantity curve of each target characteristic dimension output by the wind farm equivalent model is extracted;

[0122] For each target characteristic dimension, based on the Sobol analysis method and the electrical quantity curve of the target characteristic dimension, the sensitivity of each candidate control parameter is analyzed, and based on the sensitivity analysis result, the key control parameter of the target characteristic dimension is selected from the plurality of candidate control parameters.

[0123] Wherein, the reference control parameter is determined based on the running of the wind farm detailed model corresponding to the wind farm equivalent model for each combined sample.

[0124] In the example embodiment, in order to perform global sensitivity analysis, Sobol index method can be used. Sobol index method is a mathematical method based on variance decomposition, which is used to quantify the contribution of input parameters to the uncertainty of output results. The core idea is to decompose the total variance of the output variable into the sum of the variances of each input parameter and their interaction, so as to identify the parameters that have a significant impact on the output. The Sobol index method is defined as follows: assuming that the model output Y is a function of input parameters (X1,...,X i' ,...,X n ), X i' is the i'th input parameter, X n is then'th input parameter, and n is the number of input parameters; the model output is denoted as Y=f(X1,X2,...,X n), f() represents the functional relationship between the model output and the input parameters, then the total variance can be decomposed as:

[0125]

[0126] where Var(Y) is the total variance of the model output Y, V i' is the first-order variance of the parameter X i' (i.e., the main effect), V i' reflects the direct influence of X i' on the output independently, V i' = Var[E(Y|X i' )], E represents the expectation; i', j are the indices of the input parameters respectively; V i'j represents the interaction variance of the parameter X i' and X j (i.e., the second-order interaction effect), V i'j = Var[E(Y|X i' , X j )]-V i' -V j , V j is the first-order variance of the parameter X j ; V 1,2,...,n is the n-order interaction effect of the input parameters, and the interaction of the high-order term can be obtained in the same way, but the high-order interaction is often ignored in actual calculation due to high computational complexity. The first-order Sobol index S i' is:

[0127]

[0128] S i' is used to represent the contribution ratio of the individual action of the parameter X i' to the variance of the output.

[0129] The total Sobol index S Ti' is:

[0130] which contains the main effect of X i' and all the interaction effects with other parameters, reflecting the comprehensive contribution of X i' .

[0131] Based on the analysis and calculation process of the Sobol index method, the target characteristic dimension and the corresponding candidate control parameter are selected for the screening of the key control parameter. Exemplarily, the target characteristic dimension includes active power and reactive power; the plurality of candidate control parameters of the active power can include a variable pitch proportional gain, a maximum power point tracking time constant, and a current loop integral time in a wind turbine model, and the like. The plurality of candidate control parameters of the reactive power can include a voltage control loop proportional coefficient, a voltage control loop integral coefficient, a reactive current loop control coefficient, and a reactive-voltage droop coefficient, and the like. For each characteristic dimension, the candidate control parameter can be selected according to the actual situation. For example, the peak value or the steady-state average value of the active power at the grid connection point of the wind farm can be taken as the output Y, and the parameters of the variable pitch proportional gain, the MPPT (maximum power point tracking) time constant, the current loop integral time, and the like in the wind turbine model can be taken as the input parameter X. The quasi-Monte Carlo method (such as Sobol sequence) is used to generate a combined sample of the input parameters, which can cover the reasonable value range of each input parameter, and the parameter value range can be obtained from the detailed model technical specification. For each combined sample, the wind farm detailed model and the wind farm equivalent model are run in the simulation software (such as MATLAB / Simulink, PSCAD), and the active power dynamic curve is collected, and the peak value and the steady-state average value are extracted. The first-order Sobol index and the total Sobol index of each input parameter are calculated by using a post-processing tool (such as SALib, SobolGSA). If the total Sobol index S Ti' ≥ 0.05 (i.e., the input parameter contributes ≥ 5% to the output variance), it indicates that the parameter is a high-sensitive parameter; if it indicates that it is a significant interaction parameter. All high-sensitive parameters and / or significant interaction parameters can be taken as the key control parameters. According to the global sensitivity analysis result, the key control parameter set affecting the active power peak-to-peak value and the steady-state value in the wind farm equivalent model is extracted. The same analysis is performed on the parameter set of the detailed model, and the corresponding key control parameter set of the equivalent model is extracted.

[0132] In some embodiments, the similarity between the key control parameter of the target characteristic dimension and the corresponding reference control parameter based on the target characteristic dimension in S130 is used for parameter sensitivity consistency evaluation of the wind farm equivalent model, including:

[0133] For each target characteristic dimension, the similarity between the key control parameter of the target characteristic dimension and the corresponding reference control parameter is calculated;

[0134] If the similarity between the key control parameter of the target characteristic dimension and the corresponding reference control parameter is greater than or equal to the sensitivity threshold, it is determined that the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model in the target characteristic dimension is consistent;

[0135] If the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model to each target characteristic dimension is consistent, it is determined that the wind farm equivalent model passes the parameter sensitivity consistency evaluation.

[0136] In the present example embodiment, for each target characteristic dimension, the similarity in this dimension can be determined by calculating the Jaccard similarity coefficient between the control parameter sets corresponding to the wind farm equivalent model and the detailed model, or can be measured by vector similarity. Exemplarily, the Jaccard similarity coefficient of the key control parameter sets corresponding to the two models is calculated as follows:

[0137]

[0138] wherein PMI is the similarity of the key control parameter sets corresponding to the two models, S detail is the key control parameter set of the wind farm detailed model, S eq is the key control parameter set of the wind farm equivalent model, ∩ represents the intersection, and ∪ represents the union set. The sensitivity threshold can be 0.6-1.0, for example, the sensitivity value is 0.8, then if PMI≥0.8, it can be considered that the parameter sensitivity of the wind farm equivalent model and the detailed model is consistent. If each target characteristic dimension meets the parameter sensitivity consistency, the wind farm equivalent model passes the parameter sensitivity consistency evaluation.

[0139] In some embodiments, the determination of the accuracy of the wind farm equivalent model based on the impedance consistency evaluation result or the parameter sensitivity consistency evaluation result in S140 comprises:

[0140] For transient scenarios, the accuracy of the wind farm equivalent model is determined based on the parameter sensitivity consistency evaluation result;

[0141] For wide frequency oscillation scenarios, the accuracy of the wind farm equivalent model is determined based on the impedance consistency evaluation result.

[0142] In the example embodiment, the non-steady-state operation scenarios include transient scenarios and wide-frequency oscillation scenarios. Since the impedance characteristics are key representations of the dynamic response of the system, especially in wide-frequency oscillation scenarios (such as sub- / super-synchronous oscillation, high-frequency resonance), if the terminal impedance characteristics of the equivalent model and the detailed model are significantly different, the oscillation risk may not be accurately predicted. Therefore, in the wide-frequency oscillation scenario, a layer of impedance characteristic consistency evaluation is added, and the accuracy of the equivalent model is determined based on the evaluation result. If the key control parameters (such as the pitch proportional gain of the pitch control and the MPPT tracking time constant) affecting the key electrical quantities (such as active power) in the detailed model do not reflect the same sensitivity in the equivalent model, the dynamic response of the equivalent model may be inaccurate in the transient scenario. Therefore, in the transient scenario, a layer of parameter sensitivity consistency evaluation for the key control parameters is added, and the accuracy of the equivalent model is determined based on the evaluation result. The consistency of the terminal impedance characteristics and the parameter sensitivity is used as a necessary evaluation dimension in the present application to ensure the accuracy of the equivalent model in wide-frequency oscillation and multi-time-scale dynamic response.

[0143] In an example embodiment, after the power flow consistency evaluation of the wind farm equivalent model is performed based on the error statistics between the equivalent electrical quantity curve of the feature dimension and the corresponding reference electrical quantity curve, the method further comprises:

[0144] For the steady-state scenario, the accuracy of the wind farm equivalent model is determined based on the power flow consistency evaluation results of the multiple feature dimensions.

[0145] In the example embodiment, the operation scenarios include steady-state scenarios. Since the power system operates stably in the steady-state scenario, the accuracy of the model can be evaluated by only performing power flow consistency judgment through the multi-dimensional electrical quantity curve.

[0146] For example, for multiple scenarios, if the electrical quantity error of any feature dimension is greater than the corresponding error, it indicates that the simulation accuracy of the equivalent model is poor, which may be caused by the inconsistency of the main loop parameters or the control strategy of the wind farm equivalent model and the reference model (such as the detailed model), and the model can be improved by adjusting the main loop parameters or the control strategy of the equivalent model. If the parameter sensitivity of the wind farm equivalent model and the reference model (such as the detailed model) is inconsistent, it may be caused by the control parameters, and the corresponding control parameters can be adjusted based on the evaluation result. The above methods guide the modeling process of the wind farm equivalent model to improve the simulation performance of the wind farm equivalent model.

[0147] Simulation experiment

[0148] A detailed model and an equivalent model (single equivalent unit) of a doubly-fed wind farm are built in PSCAD to evaluate the accuracy of the equivalent model of the wind farm under transient scenarios. Control parameters: MPPT tracking time constant Tmppt=3 s, variable pitch proportional gain Kp=0.15, current loop integral time Ti=0.02 s, rotor-side current loop proportional gain Kr, DC bus voltage control integral time Tdc, and wind wheel inertia time constant H. Grid events: the grid voltage drops to 50%-100% of the rated value at 1.5 s, and recovers after 100 ms (the total simulation time is 1.8 s). The number of Monte Carlo simulations is 20, and the parameter perturbation range is Tmppt∈[1 s, 5 s] s, Kp∈[0.1, 0.2], Ti∈[0.02 s, 0.1 s] s, Kr∈[0.5, 2.0], Tdc∈[0.01 s, 0.1 s], and H∈[3 s, 6 s]. Multi-dimensional feature extraction and time window division: for the convenience of explaining multi-dimensional feature extraction and time window, the wind farm equivalent model is taken as an example, and the scenario is that the grid voltage drops to 100% of the rated value at 1.5 s, and recovers after 100 ms (the total simulation time is 1.8 s). The response time-domain waveform of the characteristic electrical quantity curve of the equivalent model is shown in FIG. 1, wherein the horizontal coordinate of each graph is time. Figures 2-5 Figure 2 is a group of three-phase current waveform graphs of the wind farm outlet grid connection point, and the vertical coordinate I abcWTG is the three-phase current of the wind farm grid connection point, and the unit is per unit (p.u.). Figure 2 It can be seen that at 1.5 s voltage drop, the three-phase current becomes small, and recovers after the fault is removed at 1.6 s. Figure 3 is a group of active power waveform graphs of the wind farm outlet grid connection point, and the vertical coordinate P WTG is the active power output by the wind farm grid connection point; from Figure 3 It can be seen that at 1.5 s voltage drop, the active power drops and becomes small, and gradually recovers after the fault is removed at 1.6 s. Figure 4 is a group of reactive power waveform graphs of the wind farm outlet grid connection point, and the vertical coordinate Q WTG is the reactive power output by the wind farm grid connection point; from Figure 4 It can be seen that at 1.5 s voltage drop, the active power does not change much during the fault period due to the support of the reactive current. Figure 5 is a group of three-phase voltage waveform graphs of the wind farm outlet grid connection point, and the vertical coordinate V abcWTG is the three-phase voltage of the wind farm grid connection point, and the unit is per unit. From Figure 5 It can be seen that the voltage is 0 due to 100% voltage drop at 1.5 s. The time window is divided into a steady-state period of 1.4-1.5 s, a fault period of 1.5-1.6 s, and a recovery period of 1.6-1.8 s.

[0149] ​The error threshold values of five types of electrical quantities are calculated by the method, and the results are shown in Table 1. As can be seen from Table 1, the relative average errors of the equivalent model of the wind farm in each characteristic dimension are relatively small (less than 5%), and the error threshold values of different characteristic dimensions are different.

[0150] Table 1

[0151]

[0152] By injecting a wideband small perturbation signal (1Hz-1kHz) into the grid-connected point of the wind farm, the machine terminal impedance of the detailed model and the equivalent model is obtained as shown in Figure 6 The horizontal coordinate f in the figure represents the frequency of the small perturbation signal injected into the grid-connected point, from 1Hz to 1kHz, and the black curve represents the grid impedance. The vertical coordinate of the upper half of the subgraph is the machine terminal impedance amplitude (Magnitude), with units of Ω, wherein the green circles represent the impedance amplitude of the detailed model at the sweep frequency, and the blue asterisks represent the impedance amplitude of the equivalent model at the sweep frequency. The vertical coordinate of the lower half of the subgraph is the phase (Phase) of the machine terminal impedance, with units of °, wherein the green circles represent the impedance phase of the detailed model at the sweep frequency, and the blue asterisks represent the impedance phase of the equivalent model at the sweep frequency. As can be seen from Figure 6 , the impedance amplitudes and phases of the equivalent model and the detailed model are basically coincident, indicating that the impedance characteristics of the two models are consistent.

[0153] The steady-state average of the active power at the grid-connected point of the wind farm is defined as the output, and the variable pitch proportional gain K p , the MPPT time constant T, the integral time of the current loop T i , the rotor-side current loop proportional gain Kr, the DC bus voltage control integral time Tdc, and the wind wheel inertia time constant H in the wind turbine model are defined as the input. The quasi-Monte Carlo method is used to generate combined samples of the input parameters, covering the reasonable range of each parameter. For each parameter sample, the detailed model and the equivalent model are run in the simulation software, the output active power dynamic curve is collected, and the peak value (P pp ) or steady-state average P avg of the active power at the grid-connected point of the wind farm is extracted. The first-order Sobol index S i and the total Sobol index S Ti of each parameter are calculated by the post-processing tool (SALib), and the calculation results are shown in Table 2.

[0154] Table 2

[0155]

[0156]

[0157] The parameters with total Sobol index ≥ 5% are considered as the key parameters affecting the active power. The final key parameter set of the detailed model is {Tmppt, Kp, Ti, Kr, Tdc}, and the key parameter set of the equivalent model is {Tmppt, Kp, Ti, Kr}. The Jaccard similarity coefficient J of the two key parameter sets is 4 / 5 = 0.80, which shows that the parameter sensitivity of the equivalent model is consistent with that of the detailed model.

[0158] In the actual simulation process, although the wind farm detailed model with reserved internal topology has high simulation accuracy, it may lead to long calculation time, difficult power flow convergence, and difficult software calculation scale to bear, etc. In addition, detailed modeling requires a large amount of wind power system information, and with the market reform, the information related to commercial secrets is not transparent. From the perspective of safe and stable operation of power system, only the external characteristics of wind power integration point need to be concerned. Therefore, the wind farm model mostly uses the wind farm equivalent model based on the coherent equivalence idea, but most of the current research focuses on verifying whether the simulation model and equivalent method are applicable, and the research on model accuracy evaluation alone is less. Model accuracy evaluation and modeling are both related and different. A series of technical standards and regulations on single machine models have been published by domestic and foreign authoritative institutions, but no widely accepted consensus has been reached. As the only wind power integration into power grid technical specification in China, the national standard GB / T 19963.1-2021 stipulates the basic requirements and test contents of system test when wind farm is integrated into power grid, but there is still room for further refinement and improvement in the simulation modeling accuracy evaluation index, etc.

[0159] According to the different sources of the comparison reference value, the evaluation based on the wind farm measured data can also be based on the evaluation of the wind farm detailed model simulation results. And based on the wind farm measured data, the current general qualitative evaluation method based on visual method is used to verify whether the operation characteristics of the wind farm meet the technical requirements of the grid connection guide, which lacks statistical significance. And in terms of wind farm measured data acquisition, it is difficult to fully traverse multiple scenarios and multiple operating conditions, especially extreme operating conditions such as extremely high wind speed grid fault, and it is difficult to face the economic cost caused by a large number of monitoring and data storage equipment. In the accuracy evaluation research based on the simulation results of the wind farm detailed model, the quantitative evaluation method is generally used. Specifically, the absolute error or relative error between the equivalent model and the detailed model simulation results is calculated to evaluate the accuracy of the wind farm equivalent model. In the new energy high proportion system, the existing accuracy evaluation method may cause the mismatch of the machine terminal impedance characteristics between the wind farm equivalent model and the detailed model in the key frequency band (such as the negative damping characteristics caused by the rotor side control of the doubly-fed wind turbine), which will lead to the misjudgment of the simulation results of the oscillation stability. At the same time, when the equivalent model ignores the sensitivity of the key parameters (such as the weakening of the sensitivity of the variable pitch parameter in the aggregation process), the time sequence consistency of the dynamic response will be destroyed. In addition, in the research of the time span of the accuracy evaluation, the existing literature often directly takes the whole transient process as the time span of the accuracy evaluation, which lacks in-depth analysis and discussion.

[0160] Based on the above analysis, the application can focus on the wind farm detailed model as the benchmark model to carry out comparative test, traverse multiple working conditions, and more comprehensively and reasonably verify the evaluation indexes under multiple scenes and multiple working conditions, and then determine the error threshold. By considering that the induction mechanism of sub- / ultra-synchronous oscillation is closely related to the equivalent machine terminal impedance of the wind farm, and the dynamic characteristics of the active power of the wind farm are coupled by the multi-stage control system, for example, the time sequence matching relationship between the MPPT tracking rate and the delay of the variable pitch response directly affects the power climbing rate. A multi-dimensional feature extraction system including five types of electrical quantities, such as active power, reactive power, voltage, current and reactive current, is established, and combined with the impedance characteristics and parameter sensitivity, the system is adapted to steady-state, transient and wideband oscillation scenes; multiple working condition error samples are generated by Monte Carlo simulation, the error confidence interval is calculated based on the normal distribution assumption, and the upper limit of the interval is rounded as the error threshold, which replaces the empirical threshold setting and has statistical significance. A wideband small disturbance is injected into the terminal of the wind farm, the impedance curve is obtained by the sweep frequency method, the impedance difference degree of the specified frequency band (sub-synchronous, medium frequency and high frequency) is calculated, and the impedance characteristic consistency is determined. The active power key parameter set of the detailed model and the equivalent model is extracted by using global sensitivity analysis (Sobol index), and the parameter sensitivity consistency is evaluated based on the Jaccard similarity coefficient, which can improve the reliability of the accuracy evaluation of the equivalent model. In summary, the application provides a wind farm equivalent model accuracy evaluation method based on multi-dimensional quantitative analysis. When selecting the evaluation indexes, the transient characteristics of the wind power system are retained, and the information redundancy is reduced as much as possible; meanwhile, the multi-time scale system characteristics are considered, and the uncertainty of the error threshold is considered when quantifying the error threshold.

[0161] The application combines the consistency evaluation of the multi-time window error threshold, the impedance characteristics and the key parameter sensitivity, constructs an evaluation framework of "multi-dimensional feature extraction-multi-scene time window division-error threshold quantization based on confidence interval-impedance characteristic consistency evaluation-parameter sensitivity consistency evaluation", supports the accuracy evaluation of the wind farm equivalent model in the wideband oscillation analysis and the dynamic control scene, and the method is suitable for simulation verification, sub- / ultra-synchronous oscillation analysis and dispatching operation decision support of large-scale wind farms connected to the power grid.

[0162] Embodiment 2

[0163] Based on the same inventive concept, the application further provides a wind farm equivalent model accuracy evaluation system, which comprises:

[0164] The multi-dimensional feature extraction module is used for simulating different running scenes of the target wind farm based on the wind farm equivalent model, and extracting the equivalent electrical quantity curves of multiple feature dimensions and the terminal equivalent impedance curve of the simulation output, respectively.

[0165] a power flow evaluation module configured to evaluate the power flow consistency of the wind farm equivalent model based on error statistics between the equivalent electrical quantity curve of each characteristic dimension and the corresponding reference electrical quantity curve;

[0166] a characteristic evaluation module configured to evaluate the impedance consistency of the wind farm equivalent model based on the difference between the equivalent impedance curve and the reference impedance curve if all the characteristic dimensions pass the power flow consistency evaluation, or to evaluate the parameter sensitivity consistency of the wind farm equivalent model based on the similarity between the key control parameter of the target characteristic dimension and the corresponding reference control parameter;

[0167] an accuracy evaluation module configured to determine the accuracy of the wind farm equivalent model based on the result of the impedance consistency evaluation or the result of the parameter sensitivity consistency evaluation;

[0168] wherein the reference electrical quantity curve and the reference impedance curve are respectively used to represent the standard output electrical quantity curve of the target wind farm in different operating scenarios; the reference control parameter is used to represent the control parameter affecting the standard output electrical quantity curve of the target wind farm, and the key control parameter is the control parameter affecting the output electrical quantity of the wind farm equivalent model; and the target characteristic dimension is at least one of the multiple characteristic dimensions.

[0169] In a possible implementation, the multiple characteristic dimensions include active power, reactive power, output voltage, output current and reactive current.

[0170] In a possible implementation, the power flow evaluation module comprises an error threshold quantification submodule configured to:

[0171] perform multiple simulations of the target wind farm in different operating scenarios by using the wind farm equivalent model, and correspondingly obtain multiple sets of equivalent electrical quantity curves with multiple characteristic dimensions;

[0172] for each characteristic dimension, generate a corresponding error sample set based on the relative average error between each sampling point on the multiple sets of equivalent electrical quantity curves of the characteristic dimension and the corresponding reference electrical quantity curve;

[0173] calculate the confidence interval of the error sample set corresponding to each characteristic dimension based on a statistical method and a preset significance level;

[0174] determine the error threshold of the wind farm equivalent model in each characteristic dimension based on the confidence interval of the error sample set corresponding to each characteristic dimension;

[0175] The power flow evaluation module further comprises:

[0176] a power flow evaluation submodule, configured to determine that the wind farm equivalent model passes the power flow consistency evaluation in the current characteristic dimension if a relative average error between the equivalent electrical quantity curve of the characteristic dimension and the corresponding reference electrical quantity curve is less than or equal to a corresponding error threshold.

[0177] In a possible implementation, the error threshold of the wind farm equivalent model in each characteristic dimension is as follows:

[0178]

[0179] wherein ε m * is the error threshold of the wind farm equivalent model in the mth characteristic dimension, is the upper limit of the confidence interval of the wind farm equivalent model in the mth characteristic dimension, is the average value of the error sample set corresponding to the mth characteristic dimension, and a is a confidence level, is the critical value of t-distribution with M-1 degrees of freedom at the confidence level a, and S m is the standard deviation of the error sample set corresponding to the mth characteristic dimension, M m is the sample quantity of the error sample set corresponding to the mth characteristic dimension, and ε i,m is the ith error sample in the error sample set corresponding to the mth characteristic dimension.

[0180] In a possible implementation, the characteristic evaluation module includes an impedance evaluation submodule, which is configured to:

[0181] determine an impedance evaluation index of the wind farm equivalent model based on a difference between the machine terminal equivalent impedance curve and the reference impedance curve at each frequency;

[0182] determine that the wind farm equivalent model passes the impedance consistency evaluation if the impedance evaluation index is less than or equal to an impedance threshold.

[0183] In a possible implementation, the characteristic evaluation module includes an impedance evaluation submodule, which is configured to:

[0184] generate a plurality of groups of combined samples of candidate control parameters for each target characteristic dimension by using a quasi-Monte Carlo method;

[0185] run the wind farm equivalent model based on each group of combined samples, and extract electrical quantity curves of each target characteristic dimension output by the wind farm equivalent model;

[0186] perform sensitivity analysis on each candidate control parameter based on a Sobol analysis method and an electrical quantity curve of the target characteristic dimension, and filter the key control parameter of the target characteristic dimension from the plurality of candidate control parameters based on a result of the sensitivity analysis;

[0187] The reference control parameter is determined based on running of a wind farm detailed model corresponding to the wind farm equivalent model based on each group of combined sample.

[0188] In a possible implementation, the target characteristic dimensions include active power and reactive power.

[0189] The plurality of candidate control parameters of the active power include a variable pitch proportional gain, a maximum power point tracking time constant, and a current loop integral time in a wind turbine generator model.

[0190] The plurality of candidate control parameters of the reactive power include a voltage control loop proportional coefficient, a voltage control loop integral coefficient, a reactive current loop control coefficient, and a reactive-voltage droop coefficient.

[0191] In a possible implementation, the characteristic evaluation module includes a parameter sensitivity evaluation submodule, which is configured to:

[0192] For each target characteristic dimension, a similarity between the key control parameter of the target characteristic dimension and the corresponding reference control parameter is calculated.

[0193] If the similarity between the key control parameter of the target characteristic dimension and the corresponding reference control parameter is greater than or equal to a sensitivity threshold, it is determined that the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model in the target characteristic dimension is consistent.

[0194] If the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model in each target characteristic dimension is consistent, it is determined that the wind farm equivalent model passes the parameter sensitivity consistency evaluation.

[0195] In a possible implementation, the operation scenarios include transient scenarios and wide frequency oscillation scenarios; and the accuracy evaluation module is specifically configured to:

[0196] For the transient scenarios, the accuracy of the wind farm equivalent model is determined based on a result of the parameter sensitivity consistency evaluation.

[0197] For the wide frequency oscillation scenarios, the accuracy of the wind farm equivalent model is determined based on a result of the impedance consistency evaluation.

[0198] In a possible implementation, the operation scenarios include steady-state scenarios; and the accuracy evaluation module is further configured to:

[0199] For a steady-state scenario, based on the consistency evaluation results of the multiple characteristic dimensions, the accuracy of the wind farm equivalent model is determined.

[0200] Embodiment 3

[0201] As shown in Figure 7 The electronic device in this embodiment can include a processor, a memory, a transceiver component, etc. The memory, the processor and the transceiver component are connected through a bus; the memory can be used to store an execution program, and the exemplary execution program can include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be called and / or modified when the instructions are executed.

[0202] The processor can be a central processing unit (CPU), and can also 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., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the storage medium to implement a corresponding method flow or a corresponding function, to implement the steps of the wind farm equivalent model accuracy evaluation method in the above embodiment.

[0203] Embodiment 4

[0204] Based on the same inventive concept, the application further provides a readable storage medium, specifically, an electronic device readable storage medium (Memory). The electronic device readable storage medium is a memory device in the electronic device, and is used for storing programs and data. It can be understood that the storage medium herein can include a built-in storage medium in the electronic device, and of course can also include an extended storage medium supported by the electronic device. The storage medium provides a storage space, and the storage space stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more execution programs (including program codes). It should be noted that the storage medium herein can be a high-speed RAM memory or a non-volatile memory such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, so as to realize the steps of the accuracy evaluation method of the wind farm equivalent model in the above embodiment.

[0205] Those skilled in the art will appreciate that embodiments of the application can be supplied as a method, a system, or a computer program product. Therefore, the application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can 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-ROMs, optical storage media, etc.) containing computer-usable program code.

[0206] The application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows 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 produce a device that implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows or blocks.

[0207] These computer program instructions can also be stored in a computer-readable memory that can guide the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows or blocks.

[0208] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are generated to realize the computer-implemented processes, and the instructions executed on the computer or other programmable devices provide a process for implementing the functions specified in the flowchart Figure 1 One flow or multiple flows and / or the functions specified in one block or multiple blocks. Figure 1 One flow or multiple flows and / or the functions specified in one block or multiple blocks.

[0209] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit the scope of protection, although the present application has been described in detail with reference to the above examples, those skilled in the art should understand: after reading the present application, the applicant can make various changes, modifications or equivalent replacements to the specific embodiments, but these changes, modifications or equivalent replacements are all within the scope of protection of the claims.

Claims

1. A method for evaluating the accuracy of an equivalent model of a wind farm, characterized in that, include: Based on the equivalent model of the wind farm, simulations of different operating scenarios were carried out on the target wind farm, and the equivalent electrical quantity curves and the equivalent impedance curve of the generator terminal in multiple characteristic dimensions of the simulation output were extracted respectively. For each feature dimension, the power flow consistency of the wind farm equivalent model is evaluated based on the error statistics between the equivalent electrical quantity curve of the feature dimension and the corresponding reference electrical quantity curve. If multiple feature dimensions pass the power flow consistency assessment, the impedance consistency assessment of the wind farm equivalent model is performed based on the difference between the equivalent impedance curve of the turbine terminal and the reference impedance curve; or, the parameter-sensitive consistency assessment of the wind farm equivalent model is performed based on the similarity between the key control parameters of the target feature dimension and the corresponding reference control parameters. The accuracy of the wind farm equivalent model is determined based on the impedance consistency assessment results or the parameter-sensitive consistency assessment results. The reference electrical quantity curve and the reference impedance curve are used to characterize the standard output electrical quantity curves of the target wind farm under different operating scenarios; the reference control parameters are used to characterize the control parameters that affect the standard output electrical quantity curves of the target wind farm, and the key control parameters are the control parameters that affect the output electrical quantity of the wind farm equivalent model; the target feature dimension is at least one of multiple feature dimensions.

2. The method according to claim 1, characterized in that, Multiple characteristic dimensions include: active power, reactive power, output voltage, output current, and reactive current.

3. The method according to claim 1, characterized in that, Before performing the power flow consistency assessment of the wind farm equivalent model based on the error statistics between the equivalent electrical quantity curves of the aforementioned feature dimensions and the corresponding reference electrical quantity curves, the following steps are also included: The target wind farm was simulated multiple times under different operating scenarios using an equivalent model of the wind farm, resulting in multiple sets of equivalent electrical quantity curves with multiple characteristic dimensions. For each feature dimension, based on the relative average error between each sampling point on multiple sets of equivalent electrical quantity curves of that feature dimension and the corresponding reference electrical quantity curve, a corresponding error sample set is generated; The confidence intervals for the error sample set corresponding to each feature dimension are calculated based on statistical methods and pre-set confidence levels. Based on the confidence interval of the error sample set corresponding to each feature dimension, the error threshold of the wind farm equivalent model in each feature dimension is determined. The power flow consistency assessment of the wind farm equivalent model based on the error statistics between the equivalent electrical quantity curves of the aforementioned feature dimensions and the corresponding reference electrical quantity curves includes: If the relative average error between the equivalent electrical quantity curve of the feature dimension and the corresponding reference electrical quantity curve is less than or equal to the corresponding error threshold, the wind farm equivalent model is determined to have passed the power flow consistency assessment in the current feature dimension.

4. The method according to claim 3, characterized in that, The error thresholds for each feature dimension of the wind farm equivalent model are as follows: Where, ε m * Let m be the error threshold of the equivalent model of the wind farm in the m-th feature dimension. This represents the upper limit of the confidence interval for the m-th feature dimension of the wind farm equivalent model. Let be the average value of the error sample set corresponding to the m-th feature dimension, and α be the confidence level. S is the critical value of the t-distribution with M-1 degrees of freedom at confidence level α. m M represents the standard deviation of the error sample set corresponding to the m-th feature dimension. m Let ε be the number of samples in the error sample set corresponding to the m-th feature dimension. i,m Let be the i-th error sample in the error sample set corresponding to the m-th feature dimension.

5. The method according to claim 1, characterized in that, The impedance consistency assessment of the wind farm equivalent model is performed based on the difference between the equivalent impedance curve at the turbine terminal and the reference impedance curve, including: Based on the difference between the equivalent impedance curve at the turbine terminal and the reference impedance curve at various frequencies, the impedance evaluation index of the wind farm equivalent model is determined. If the impedance assessment index is less than or equal to the impedance threshold, the wind farm equivalent model is determined to have passed the impedance consistency assessment.

6. The method according to claim 1, characterized in that, Before performing parameter-sensitive consistency assessment of the wind farm equivalent model based on the similarity between the key control parameters and the corresponding baseline control parameters based on the target feature dimension, the method further includes: For multiple candidate control parameters in each target feature dimension, a quasi-Monte Carlo method is used to generate combined samples of multiple candidate control parameters; Based on each set of combined samples, the wind farm equivalent model is run to extract the electrical quantity curves of each target feature dimension output by the wind farm equivalent model; For each target feature dimension, a sensitivity analysis is performed on each candidate control parameter based on the Sobol analysis method and the electrical quantity curve of the target feature dimension. Based on the sensitivity analysis results, the key control parameters of the target feature dimension are selected from multiple candidate control parameters. The baseline control parameters are determined based on the detailed wind farm model corresponding to the equivalent wind farm model for each set of combined samples.

7. The method according to claim 6, characterized in that, The target feature dimensions include active power and reactive power; The candidate control parameters for active power include the pitch proportional gain, maximum power point tracking time constant, and current loop integral time in the wind turbine model. The candidate control parameters for reactive power include the proportional coefficient of the voltage control loop, the integral coefficient of the voltage control loop, the control coefficient of the reactive current loop, and the reactive-voltage droop coefficient.

8. The method according to claim 6, characterized in that, The similarity between the key control parameters based on the target feature dimension and the corresponding benchmark control parameters is used to perform parameter-sensitive consistency evaluation on the wind farm equivalent model, including: For each target feature dimension, calculate the similarity between the key control parameters of the target feature dimension and the corresponding benchmark control parameters; If the similarity between the key control parameters of the target feature dimension and the corresponding benchmark control parameters is greater than or equal to the sensitivity threshold, it is determined that the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model is consistent in the target feature dimension. If the wind farm equivalent model and the wind farm detailed model have the same parameter sensitivity for each target feature dimension, the wind farm equivalent model is determined to have passed the parameter sensitivity consistency assessment.

9. The method according to claim 1, characterized in that, The operating scenarios include transient scenarios and broadband oscillation scenarios; determining the accuracy of the wind farm equivalent model based on impedance consistency assessment results or parameter-sensitive consistency assessment results includes: For transient scenarios, the accuracy of the wind farm equivalent model is determined based on the parameter-sensitive consistency assessment results. For wideband oscillation scenarios, the accuracy of the wind farm equivalent model is determined based on the impedance consistency assessment results.

10. The method according to claim 1, characterized in that, The operating scenarios include steady-state scenarios; after evaluating the power flow consistency of the wind farm equivalent model based on the error statistics between the equivalent electrical quantity curves of the aforementioned feature dimensions and the corresponding benchmark electrical quantity curves, the system further includes: For steady-state scenarios, the accuracy of the wind farm equivalent model is determined based on the power flow consistency assessment results of multiple feature dimensions.

11. An accuracy evaluation system for an equivalent model of a wind farm, characterized in that, include: The multi-dimensional feature extraction module is used to simulate different operating scenarios of the target wind farm based on the wind farm equivalent model, and extract the equivalent electrical quantity curves and the equivalent impedance curve of the generator terminal from multiple feature dimensions of the simulation output. The power flow assessment module is used to assess the power flow consistency of the wind farm equivalent model for each feature dimension based on the error statistics between the equivalent electrical quantity curve of the feature dimension and the corresponding reference electrical quantity curve. The feature evaluation module is used to evaluate the impedance consistency of the wind farm equivalent model based on the difference between the equivalent impedance curve of the turbine terminal and the reference impedance curve if multiple feature dimensions pass the power flow consistency evaluation; or to evaluate the parameter-sensitive consistency of the wind farm equivalent model based on the similarity between the key control parameters of the target feature dimension and the corresponding reference control parameters. An accuracy evaluation module is used to determine the accuracy of the wind farm equivalent model based on impedance consistency assessment results or parameter sensitivity consistency assessment results. The reference electrical quantity curve and the reference impedance curve are used to characterize the standard output electrical quantity curves of the target wind farm under different operating scenarios; the reference control parameters are used to characterize the control parameters that affect the standard output electrical quantity curves of the target wind farm, and the key control parameters are the control parameters that affect the output electrical quantity of the wind farm equivalent model; the target feature dimension is at least one of multiple feature dimensions.

12. The system according to claim 11, characterized in that, Multiple characteristic dimensions include: active power, reactive power, output voltage, output current, and reactive current.

13. The system according to claim 11, characterized in that, The power flow assessment module includes an error threshold quantization submodule, which is used for: The target wind farm was simulated multiple times under different operating scenarios using an equivalent model of the wind farm, resulting in multiple sets of equivalent electrical quantity curves with multiple characteristic dimensions. For each feature dimension, based on the relative average error between each sampling point on multiple sets of equivalent electrical quantity curves of that feature dimension and the corresponding reference electrical quantity curve, a corresponding error sample set is generated; The confidence intervals for the error sample set corresponding to each feature dimension are calculated based on statistical methods and pre-set confidence levels. Based on the confidence interval of the error sample set corresponding to each feature dimension, the error threshold of the wind farm equivalent model in each feature dimension is determined. The current assessment module also includes: The power flow assessment submodule is used to determine that the wind farm equivalent model passes the power flow consistency assessment in the current feature dimension if the relative average error between the equivalent electrical quantity curve of the feature dimension and the corresponding reference electrical quantity curve is less than or equal to the corresponding error threshold.

14. The system according to claim 13, characterized in that, The error thresholds for each feature dimension of the wind farm equivalent model are as follows: Where, ε m * Let m be the error threshold of the equivalent model of the wind farm in the m-th feature dimension. This represents the upper limit of the confidence interval for the m-th feature dimension of the wind farm equivalent model. Let be the average value of the error sample set corresponding to the m-th feature dimension, and α be the confidence level. S is the critical value of the t-distribution with M-1 degrees of freedom at confidence level α. m M represents the standard deviation of the error sample set corresponding to the m-th feature dimension. m Let ε be the number of samples in the error sample set corresponding to the m-th feature dimension. i,m Let be the i-th error sample in the error sample set corresponding to the m-th feature dimension.

15. The system according to claim 11, characterized in that, The characteristic evaluation module includes an impedance evaluation submodule, which is used for: Based on the difference between the equivalent impedance curve at the turbine terminal and the reference impedance curve at various frequencies, the impedance evaluation index of the wind farm equivalent model is determined. If the impedance assessment index is less than or equal to the impedance threshold, the wind farm equivalent model is determined to have passed the impedance consistency assessment.

16. The system according to claim 11, characterized in that, It also includes a key parameter filtering module, which is used for: For multiple candidate control parameters in each target feature dimension, a quasi-Monte Carlo method is used to generate combined samples of multiple candidate control parameters; Based on each set of combined samples, the wind farm equivalent model is run to extract the electrical quantity curves of each target feature dimension output by the wind farm equivalent model; For each target feature dimension, a sensitivity analysis is performed on each candidate control parameter based on the Sobol analysis method and the electrical quantity curve of the target feature dimension. Based on the sensitivity analysis results, the key control parameters of the target feature dimension are selected from multiple candidate control parameters. The baseline control parameters are determined based on the detailed wind farm model corresponding to the equivalent wind farm model for each set of combined samples.

17. The system according to claim 16, characterized in that, The target feature dimensions include active power and reactive power; The candidate control parameters for active power include the pitch proportional gain, maximum power point tracking time constant, and current loop integral time in the wind turbine model. The candidate control parameters for reactive power include the proportional coefficient of the voltage control loop, the integral coefficient of the voltage control loop, the control coefficient of the reactive current loop, and the reactive-voltage droop coefficient.

18. The system according to claim 16, characterized in that, The characteristic evaluation module includes a parameter sensitivity evaluation submodule, which is used for: For each target feature dimension, calculate the similarity between the key control parameters of the target feature dimension and the corresponding benchmark control parameters; If the similarity between the key control parameters of the target feature dimension and the corresponding benchmark control parameters is greater than or equal to the sensitivity threshold, it is determined that the parameter sensitivity of the wind farm equivalent model and the wind farm detailed model is consistent in the target feature dimension. If the wind farm equivalent model and the wind farm detailed model have the same parameter sensitivity for each target feature dimension, the wind farm equivalent model is determined to have passed the parameter sensitivity consistency assessment.

19. The system according to claim 11, characterized in that, The operating scenarios include transient scenarios and wideband oscillation scenarios; the accuracy evaluation module is specifically used for: For transient scenarios, the accuracy of the wind farm equivalent model is determined based on the parameter-sensitive consistency assessment results. For wideband oscillation scenarios, the accuracy of the wind farm equivalent model is determined based on the impedance consistency assessment results.

20. The system according to claim 11, characterized in that, The operating scenario includes a steady-state scenario; the accuracy evaluation module is also used for: For steady-state scenarios, the accuracy of the wind farm equivalent model is determined based on the power flow consistency assessment results of multiple feature dimensions.

21. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method as described in any one of claims 1 to 10 is implemented.

22. A readable storage medium, characterized in that, It contains an executable program, which, when executed, implements the method as described in any one of claims 1 to 10.

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