Power grid security checking method and device for wind power plant equivalence

Abstract

The invention discloses a power grid security checking method and device for wind power plant equivalence, which are used for solving the problem that a calculation result cannot meet a security checking requirement possibly caused by an electromechanical transient model and parameter deviation of a low-voltage ride-through characteristic in wind power plant equivalence. The method comprises the following steps: acquiring low-pass characteristic data of each wind turbine generator under the condition that different short-circuit faults occur in a power system; for each wind turbine generator set, constructing a reference model of the wind turbine generator set according to the low-pass characteristic data, and performing parameter updating on the reference model based on the maximum allowable value of the average absolute deviation to obtain a check model; carrying out equivalence based on the plurality of reference models to obtain a low-voltage crossing characteristic reference model of the wind power plant, and carrying out equivalence based on the plurality of check models to obtain a low-voltage crossing characteristic check model of the wind power plant; and carrying out error checking on the low-voltage crossing characteristic reference model by adopting the low-voltage crossing characteristic checking model, and if the checking is passed, applying the low-voltage crossing characteristic reference model to power grid safety checking.

Description

Technical Field

[0001] The present invention relates to the technical field of power system equivalence, and in particular to a power grid safety verification method for wind farm equivalence, a power grid safety verification device for wind farm equivalence, an electronic device and a storage medium. Background Art

[0002] With the rapid development of new power systems based on new energy, the scale of new energy units is getting larger and larger, and the impact of their transient characteristics under fault conditions on the power system can no longer be ignored. The low voltage ride-through characteristics of wind farms under fault conditions are one of the important transient characteristics of wind farms. If the low voltage ride-through parameters of wind turbines in wind farms are not set accurately, there will be a large deviation between the electromechanical transient characteristics of the simulated power system and the electromechanical transient characteristics of the actual power system. It is difficult to meet the accuracy requirements for power system safety verification.

[0003] The commonly used electromechanical transient modeling method for wind farms is the aggregation method. The main principle is to equate the wind turbines in the wind farm into one or several units. The equivalent wind turbine model retains or basically retains the model structure of the original unit. The modeling of the low voltage ride-through characteristics of wind turbines in the wind farm generally first obtains the low voltage ride-through characteristic curve of the wind turbine through actual measurement, and then obtains the low voltage ride-through simulation model and parameters of the wind turbine through fitting and other methods. The relevant power standards have specific requirements for the verification of the electromechanical transient model and parameters of the low voltage ride-through characteristics of wind turbines, including verification conditions, measured data, verification steps and verification result evaluation. Only the electromechanical transient models and parameters of the low voltage ride-through characteristics of wind turbines that meet the verification result evaluation requirements can be used for electromechanical transient simulation calculations of power systems.

[0004] Since different technicians use different modeling methods, the electromechanical transient models and parameters of the low voltage ride-through characteristics that meet the requirements of relevant power standards for the same wind turbine low voltage ride-through characteristics measured data will also be different. Different electromechanical transient models and parameters of low voltage ride-through characteristics may produce different simulation results in the power system safety verification. This can easily lead to the problem that the simulation results do not meet the safety verification requirements. Summary of the invention

[0005] The present invention provides a power grid safety verification method for wind farm equivalent, a power grid safety verification device for wind farm equivalent, an electronic device and a storage medium, which are used to solve or partially solve the technical problem that the electromechanical transient model and parameter deviation of the low voltage ride-through characteristics in the wind farm equivalent may cause the calculation results to fail to meet the safety verification requirements.

[0006] The present invention provides a power grid security verification method for wind farm equivalence, wherein the wind farm includes a plurality of wind turbines, and the method comprises:

[0007] Acquiring low-throttle characteristic data of each wind turbine generator set under different short-circuit fault conditions in the power system;

[0008] For each wind turbine generator set, a benchmark model of the wind turbine generator set is constructed according to the low penetration characteristic data, and parameters of the benchmark model are updated based on a maximum allowable mean absolute deviation to obtain a verification model;

[0009] Performing equalization based on a plurality of the reference models to obtain a low penetration characteristic reference model of the wind farm, and performing equalization based on a plurality of the verification models to obtain a low penetration characteristic verification model of the wind farm;

[0010] The low-throat characteristic verification model is used to perform an error check on the low-throat characteristic reference model. If the check passes, the low-throat characteristic reference model is used for power grid safety verification.

[0011] Optionally, constructing a benchmark model of the wind turbine generator set according to the low penetration characteristic data includes:

[0012] Constructing a low-power-through characteristic model of the wind turbine generator set; the low-power-through characteristic model includes a low-power-through active model and a low-power-through reactive model; the low-power-through active model is used to describe the low-power-through active characteristic of the wind turbine generator set, and the low-power-through reactive model is used to describe the low-power-through reactive characteristic of the wind turbine generator set;

[0013] With the goal of minimizing the deviation between the set low-through simulation data and the low-through characteristic data, the least square method is used to solve the low-through active model and the low-through reactive model respectively, and the solved active characteristic model parameters are used as active reference model parameters to update the low-through active model, and the solved reactive characteristic model parameters are used as reactive reference model parameters to update the low-through reactive model;

[0014] The updated low-voltage active model and the updated low-voltage reactive model are used as the benchmark models of the wind turbine generator set.

[0015] Optionally, the active reference model parameters include an active voltage coefficient and an active recovery coefficient, and the reactive reference model parameters include a reactive voltage reference coefficient and a reactive recovery reference coefficient; and the step of updating the reference model parameters based on the maximum allowable mean absolute deviation to obtain a verification model includes:

[0016] Calculating, according to the low-throughput characteristic data, a first active average absolute deviation set and a first reactive average absolute deviation set during a fault period, and a second active average absolute deviation set and a second reactive average absolute deviation set during a post-fault recovery period;

[0017] In combination with the first active mean absolute deviation set, the active voltage reference coefficient is updated with parameters, and in combination with the second active mean absolute deviation set, the active recovery reference coefficient is updated with parameters to obtain an active verification model corresponding to the updated low-throughput active model;

[0018] The reactive voltage reference coefficient is updated in combination with the first reactive mean absolute deviation set, and the reactive recovery reference coefficient is updated in combination with the second reactive mean absolute deviation set to obtain a reactive verification model corresponding to the updated low-through reactive model;

[0019] The active power verification model and the reactive power verification model are used as verification models of the wind turbine generator set.

[0020] Optionally, the updating of the parameters of the active voltage reference coefficient in combination with the first active mean absolute deviation set, and the updating of the parameters of the active recovery reference coefficient in combination with the second active mean absolute deviation set, to obtain an active verification model corresponding to the updated low-wear active model, includes:

[0021] Performing parameter updating on the active voltage reference coefficient based on a preset active voltage update amount;

[0022] Calculating a first active mean absolute deviation update set during the fault period based on the updated active voltage coefficient;

[0023] When the maximum deviation in the first active mean absolute deviation update set is less than the preset active deviation, the active voltage coefficient is continuously updated until the maximum deviation obtained by the last update is greater than or equal to the preset active deviation, and the target active voltage coefficient is output;

[0024] Performing parameter updating on the active power recovery reference coefficient based on a preset active power recovery update amount;

[0025] calculating a second active mean absolute deviation update set during a post-fault recovery period based on the updated active restoration coefficient;

[0026] When the maximum deviation in the second active mean absolute deviation update set is less than the preset active deviation, continue to update the active recovery coefficient until the maximum deviation obtained by the last update is greater than or equal to the preset active deviation, and output the target active recovery coefficient;

[0027] The target active voltage coefficient and the target active recovery coefficient are substituted into the updated low-breakdown active model for parameter replacement to obtain an active verification model.

[0028] Optionally, the updating of the reactive voltage reference coefficient in combination with the first reactive mean absolute deviation set, and the updating of the reactive recovery reference coefficient in combination with the second reactive mean absolute deviation set, to obtain a reactive verification model corresponding to the updated low-through reactive model, includes:

[0029] Performing parameter updating on the reactive voltage reference coefficient based on a preset reactive voltage update amount;

[0030] Calculate a first reactive mean absolute deviation update set during the fault period based on the updated reactive voltage coefficient;

[0031] When the maximum deviation in the first reactive average absolute deviation update set is less than the preset reactive deviation, the reactive voltage coefficient is continuously updated until the maximum deviation obtained by the last update is greater than or equal to the preset reactive deviation, and the target reactive voltage coefficient is output;

[0032] Performing parameter updating on the reactive power recovery reference coefficient based on a preset reactive power recovery update amount;

[0033] calculating a second reactive mean absolute deviation update set during a post-fault restoration period based on the updated reactive restoration coefficient;

[0034] When the maximum deviation in the second reactive mean absolute deviation update set is less than the preset reactive deviation, the reactive recovery coefficient is continuously updated until the maximum deviation obtained by the last update is greater than or equal to the preset reactive deviation, and the target reactive recovery coefficient is output;

[0035] The target reactive voltage coefficient and the target reactive recovery coefficient are substituted into the updated low-breakthrough reactive model for parameter replacement to obtain a reactive verification model.

[0036] Optionally, performing equivalent evaluation based on a plurality of the benchmark models to obtain a low penetration characteristic benchmark model of the wind farm, and performing equivalent evaluation based on a plurality of the verification models to obtain a low penetration characteristic verification model of the wind farm, comprises:

[0037] The low penetration characteristic model of each wind turbine set is set as a benchmark model to obtain a plurality of benchmark wind turbine sets, and the plurality of benchmark wind turbine sets are converted into one set by an aggregation method to obtain a low penetration characteristic benchmark model of the wind farm;

[0038] The low penetration characteristic model of each wind turbine is set as a verification model to obtain multiple verification wind turbines, and the multiple verification wind turbines are combined into one unit through aggregation method to obtain the low penetration characteristic verification model of the wind farm.

[0039] Optionally, the adopting the low-throw characteristic verification model to perform an error check on the low-throw characteristic reference model, and if the check passes, using the low-throw characteristic reference model for power grid security verification, includes:

[0040] Performing simulation calculations for power grid safety verification using the low penetration characteristic benchmark model to obtain benchmark simulation results;

[0041] Performing simulation calculation of power grid safety verification through the low penetration characteristic verification model to obtain verification simulation results;

[0042] The error between the benchmark simulation result and the verification simulation result is calculated, and if the error is within a preset error range, the low penetration characteristic benchmark model is used for power grid security verification.

[0043] The present invention also provides a power grid safety verification device for wind farm equivalence, wherein the wind farm includes a plurality of wind turbines, and the device comprises:

[0044] A data acquisition module, used to acquire low-throttle characteristic data of each wind turbine generator set under different short-circuit fault conditions in the power system;

[0045] A model building module, for building a benchmark model of each wind turbine generator set according to the low penetration characteristic data, and updating the parameters of the benchmark model based on the maximum allowable value of the mean absolute deviation to obtain a verification model;

[0046] An equivalent processing module, used for performing equivalent processing based on a plurality of the reference models to obtain a low-through characteristic reference model of the wind farm, and performing equivalent processing based on a plurality of the verification models to obtain a low-through characteristic verification model of the wind farm;

[0047] The verification module is used to use the low-throttle characteristic verification model to perform error verification on the low-throttle characteristic reference model. If the verification passes, the low-throttle characteristic reference model is used for power grid safety verification.

[0048] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0049] The memory is used to store program code and transmit the program code to the processor;

[0050] The processor is used to execute the power grid security verification method for wind farm equivalence as described in any one of the above items according to the instructions in the program code.

[0051] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program codes, and the program codes are used to execute the power grid security verification method for wind farm equivalence as described in any one of the above items.

[0052] It can be seen from the above technical solutions that the present invention has the following advantages:

[0053] A grid safety verification method for wind farm equivalence is provided. First, the low-throughput characteristic data of each wind turbine under different short-circuit fault conditions in the power system is obtained; then, for each wind turbine, a benchmark model of the wind turbine is constructed according to the low-throughput characteristic data, and the parameters of the benchmark model are updated based on the maximum allowable value of the mean absolute deviation to obtain a verification model; then, equivalence is performed based on multiple benchmark models to obtain a low-throughput characteristic benchmark model of the wind farm, and equivalence is performed based on multiple verification models to obtain a low-throughput characteristic verification model of the wind farm; finally, the low-throughput characteristic verification model is used to perform an error check on the low-throughput characteristic benchmark model, and if the check passes, the low-throughput characteristic benchmark model is used for grid safety verification.

[0054] Thus, by constructing a wind turbine benchmark model and obtaining a verification model based on parameter updates, combined with equivalent processing, a wind farm equivalent low-voltage ride-through characteristic benchmark model and a low-voltage ride-through characteristic verification model can be obtained. By using the equivalent verification model to perform an error check on the equivalent benchmark model, it can be determined whether the equivalent benchmark model can be applied to power grid safety verification. When the equivalent benchmark model that has passed the test is used for power grid simulation calculation, a calculation result that meets the requirements of power grid safety verification can be obtained. The technical solution of the present invention can better handle the impact of the electromechanical transient model of the low voltage ride-through characteristics of the wind farm and parameter changes on the safety verification of the power system, improve the equivalent accuracy of the wind farm, and meet the accuracy requirements of the safety verification of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 is a flow chart of steps of a power grid security verification method for wind farm equivalent;

[0057] Figure 2 (a) is a schematic diagram of the active power characteristic curve after a wind farm fault occurs;

[0058] Figure 2 (b) is a schematic diagram of the reactive power characteristic curve after a wind farm fault occurs;

[0059] Figure 3 It is a schematic diagram of the overall process of a power grid security verification method for wind farm equivalent;

[0060] Figure 4 The present invention is a structural block diagram of a power grid security verification device for wind farm equivalence. DETAILED DESCRIPTION

[0061] Embodiments of the present invention provide a power grid safety verification method for wind farm equivalents, a power grid safety verification device for wind farm equivalents, an electronic device and a storage medium, which are used to solve or partially solve the technical problem that the electromechanical transient model and parameter deviation of the low voltage ride-through characteristics in the wind farm equivalents may cause the calculation results to fail to meet the safety verification requirements.

[0062] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] As an example, the commonly used electromechanical transient modeling method for wind farms is the aggregation method. The main principle is to equate the wind turbines in the wind farm into one or several units. The equivalent wind turbine model retains or basically retains the model structure of the original unit. The modeling of the low voltage ride-through characteristics of wind turbines in the wind farm generally first obtains the low voltage ride-through characteristic curve of the wind turbine through actual measurement, and then obtains the low voltage ride-through simulation model and parameters of the wind turbine through fitting and other methods. The relevant power standards have specific requirements for the verification of the electromechanical transient model and parameters of the low voltage ride-through characteristics of wind turbines, including verification conditions, measured data, verification steps and verification result evaluation. Only the electromechanical transient models and parameters of the low voltage ride-through characteristics of wind turbines that meet the verification result evaluation requirements can be used for electromechanical transient simulation calculations of power systems.

[0064] Since different technicians use different modeling methods, the electromechanical transient models and parameters of the low voltage ride-through characteristics that meet the requirements of relevant power standards for the same wind turbine low voltage ride-through characteristics measured data will also be different. Different electromechanical transient models and parameters of low voltage ride-through characteristics may produce different simulation results in the power system safety verification. This can easily lead to the problem that the simulation results do not meet the safety verification requirements.

[0065] Therefore, one of the core invention points of the embodiment of the present invention is: in order to overcome the problem that the low voltage ride-through characteristic electromechanical transient model and parameter deviation in the current wind farm equivalence may not meet the safety verification requirements, a wind farm equivalence method suitable for safety verification is provided, specifically, a power grid safety verification method for wind farm equivalence. First, the low voltage ride-through characteristic data of each wind turbine under different short-circuit faults in the power system is obtained; then, for each wind turbine, a benchmark model of the wind turbine is constructed according to the low voltage ride-through characteristic data, and the benchmark model is updated based on the maximum allowable value of the mean absolute deviation to obtain a verification model; then, based on multiple benchmark models, equivalence is performed to obtain a low voltage ride-through characteristic benchmark model of the wind farm, and based on multiple verification models, equivalence is performed to obtain a low voltage ride-through characteristic verification model of the wind farm; finally, the low voltage ride-through characteristic verification model is used to perform an error check on the low voltage ride-through characteristic benchmark model. If the check passes, the low voltage ride-through characteristic benchmark model is used for power grid safety verification.

[0066] Thus, by constructing a wind turbine benchmark model and obtaining a verification model based on parameter updates, combined with equivalent processing, a wind farm equivalent low-voltage ride-through characteristic benchmark model and a low-voltage ride-through characteristic verification model can be obtained. By using the equivalent verification model to perform an error check on the equivalent benchmark model, it can be determined whether the equivalent benchmark model can be applied to power grid safety verification. When the equivalent benchmark model that has passed the test is used for power grid simulation calculation, a calculation result that meets the requirements of power grid safety verification can be obtained. The technical solution of the present invention can better handle the impact of the electromechanical transient model of the low voltage ride-through characteristics of the wind farm and parameter changes on the safety verification of the power system, improve the equivalent accuracy of the wind farm, and meet the accuracy requirements of the safety verification of the power system.

[0067] Reference Figure 1 , shows a flowchart of a method for checking the safety of a power grid equivalent to a wind farm provided by an embodiment of the present invention, which may specifically include the following steps:

[0068] Step 101, obtaining low-breakdown characteristic data of each wind turbine generator set under different short-circuit fault conditions in the power system;

[0069] In actual situations, a wind farm may contain multiple wind turbines. First, the low voltage ride-through (LVRT) characteristic data of wind turbines under different short-circuit fault conditions in the power system can be obtained through actual measurements.

[0070] Assume that these measured data of low-through characteristics under different short-circuit fault conditions constitute the measured data set .in, It is The measured data under each fault (including 5 groups of wind turbine terminal voltage, active power, reactive power, active current and reactive current), is the total number of measured data.

[0071] Then, the low-power characteristic model and parameters of the wind turbine are determined. Including active characteristic model and parameters, as well as reactive characteristic model and parameters. Among them, the active characteristic after the wind farm fails is shown in Figure 2 (a). The reactive characteristic after the wind farm fails is shown in Figure 2 (b).

[0072] Step 102: for each wind turbine generator set, a reference model of the wind turbine generator set is constructed according to the low penetration characteristic data, and parameters of the reference model are updated based on a maximum allowable mean absolute deviation to obtain a verification model;

[0073] In some embodiments, building a benchmark model of a wind turbine generator system according to the low penetration characteristic data can be achieved by executing the following sub-steps S01 to S03:

[0074] Step S01: construct a low-power penetration characteristic model of a wind turbine; the low-power penetration characteristic model includes a low-power penetration active model and a low-power penetration reactive model; the low-power penetration active model is used to describe the low-power penetration active characteristic of the wind turbine, and the low-power penetration reactive model is used to describe the low-power penetration reactive characteristic of the wind turbine.

[0075] The active power characteristic model and parameters after the fault (low-breakdown active power model P(t)) can be described by equation (1):

[0076] (1)

[0077] In the formula, yes The voltage amplitude of the wind turbine terminal at the moment; is the active current of the wind turbine before the fault; is the rated capacity of the wind turbine; is the time when the fault disappears, is the time for active power to recover to the active power before the fault; the parameters to be determined are , , , and ;in, is the active voltage coefficient; is the active current coefficient; is the active current setting value; is the active power restoration coefficient; It is the active power recovery value.

[0078] Formula (2) is used to describe the reactive power characteristic model and parameters after the fault (low-through reactive power model Q (t)):

[0079] (2)

[0080] In the formula, It is the given value for the wind turbine to enter low voltage ride-through, generally 0.9pu; is the reactive current of the wind turbine before the fault; the parameters to be determined are , , , and ;in, is the reactive voltage coefficient; is the reactive current coefficient; is the reactive current setting value; is the reactive power restoration coefficient; It is the reactive power recovery value.

[0081] Step S02: Set the low-power penetration simulation data, take the minimum deviation between the low-power penetration simulation data and the low-power penetration characteristic data as the goal, use the least squares method to solve the low-power penetration active model and the low-power penetration reactive model respectively, and use the solved active characteristic model parameters as the active benchmark model parameters to update the low-power penetration active model, and use the solved reactive characteristic model parameters as the reactive benchmark model parameters to update the low-power penetration reactive model.

[0082] The low penetration simulation data and the measured data set are set The goal is to minimize the deviation of . By using the least squares method to solve equations (1) and (2), we can obtain the active characteristic model parameters and reactive characteristic model parameters. They are called benchmark model parameters. The solved active benchmark model parameters are recorded as , , , and , update the low-breakthrough active model. The parameters of the reactive benchmark model obtained are recorded as , , , and , update the low-penetration reactive power model.

[0083] Step S03: using the updated low-voltage active model and the updated low-voltage reactive model as the reference model of the wind turbine generator set.

[0084] The updated low-power penetration active model and the updated low-power penetration reactive model can be used as benchmark models for wind turbines.

[0085] Combined with the previous discussion, it can be seen that the active reference model parameters can include the active voltage reference coefficient And active power recovery reference coefficient , the reactive power reference model parameters include reactive power voltage reference coefficient And reactive power recovery reference coefficient .

[0086] In some embodiments, the process of updating the parameters of the reference model based on the maximum allowable value of the mean absolute deviation to obtain the calibration model may include the following sub-steps S11 to S14:

[0087] Step S11: Calculate a first active average absolute deviation set and a first reactive average absolute deviation set during a fault period, and a second active average absolute deviation set and a second reactive average absolute deviation set during a post-fault recovery period according to the low-throughput characteristic data.

[0088] The first is the calculation of the deviation set related to the low-power active characteristic.

[0089] Based on the benchmark model parameters solved above, the measured data set , calculate the active mean absolute deviation set during the fault period In order to distinguish it from the active average absolute deviation set during the post-fault recovery period below, it is written as the first active average absolute deviation set. . It is The simulation results of active power based on the benchmark model during the fault period are shown in Figure 1. data, No. The measured active power during the fault period data, is the total number of data during the failure period.

[0090] Based on the benchmark model parameters solved above, the measured data set , calculate the active mean absolute deviation set during the post-fault recovery period To distinguish it from the above fault period active average absolute deviation set, it is written as the second active average absolute deviation set. . It is The simulation results of active power based on the baseline model during the post-fault recovery period are shown in Figure 1. data, No. The active power measurement results during the post-fault recovery period are data, It is the total number of data during the recovery period after a failure.

[0091] The second is the calculation of the relevant deviation set of low-power reactive characteristics.

[0092] Based on the benchmark model parameters solved above, the measured data set , the reactive power mean absolute deviation set during the fault period is calculated To distinguish it from the reactive power average absolute deviation set during the post-fault recovery period below, it is written as the first reactive power average absolute deviation set. . It is The reactive power simulation results based on the benchmark model during the fault period data, No. The reactive power measurement results during the fault period data, is the total number of data during the failure period.

[0093] Based on the benchmark model parameters solved above, the measured data set , the reactive power mean absolute deviation set during the post-fault recovery period is calculated To distinguish it from the reactive power mean absolute deviation set during the fault period above, it is written as the second reactive power mean absolute deviation set. . It is The reactive power simulation results based on the benchmark model during the post-fault recovery period are shown in Figure 1. data, No. The reactive power measurement results during the post-fault recovery period are data, It is the total number of data during the recovery period after a failure.

[0094] Step S12: updating the parameters of the active voltage reference coefficient in combination with the first active mean absolute deviation set, and updating the parameters of the active recovery reference coefficient in combination with the second active mean absolute deviation set, to obtain an active verification model corresponding to the updated low-breakthrough active model.

[0095] Further, in some embodiments, step S12 may be implemented by executing the following sub-steps S121 to S127:

[0096] Step S121: updating the active voltage reference coefficient based on the preset active voltage update amount.

[0097] Update the active characteristic model parameters in the baseline model The update formula is: .in, It is After the update, the parameters is the number of updates, the initial value is The corresponding active voltage reference coefficient is , is the update amount each time, usually .

[0098] Step S122: Calculate a first active mean absolute deviation update set during the fault period based on the updated active voltage coefficient.

[0099] Based on the updated , for the measured data set , calculate the corresponding active mean absolute deviation set .

[0100] Step S123: When the maximum deviation in the first active mean absolute deviation update set is less than the preset active deviation, continue to update the active voltage coefficient until the maximum deviation obtained by the last update is greater than or equal to the preset active deviation, and output the target active voltage coefficient.

[0101] set up is the active mean absolute deviation set The maximum deviation in .if , follow the above steps to continue updating ;if , then the updated parameters Recorded as .

[0102] Step S124: updating the active power recovery reference coefficient based on the preset active power recovery update amount.

[0103] Update the active characteristic model parameters in the baseline model The update formula is: .in, It is After the update, the parameters is the number of updates, the initial value is The corresponding active power recovery reference coefficient , is the update amount each time, usually .

[0104] Step S125: Calculate a second active mean absolute deviation update set during the post-fault recovery period based on the updated active restoration coefficient.

[0105] Based on the updated , for the measured data set , calculate the corresponding active mean absolute deviation set .

[0106] Step S126: When the maximum deviation in the second active mean absolute deviation update set is less than the preset active deviation, continue to update the active recovery coefficient until the maximum deviation obtained by the last update is greater than or equal to the preset active deviation, and output the target active recovery coefficient.

[0107] set up is the active mean absolute deviation set The maximum deviation in .if , follow the above steps to continue updating ;if , then the updated parameters Recorded as .

[0108] Step S127: Substitute the target active voltage coefficient and the target active recovery coefficient into the updated low-breakthrough active model for parameter replacement to obtain an active verification model.

[0109] The parameters of the active characteristic model in the benchmark model are Updated to ,Will Updated to The updated model is called the active power verification model.

[0110] Step S13: updating the parameters of the reactive voltage reference coefficient in combination with the first reactive mean absolute deviation set, and updating the parameters of the reactive recovery reference coefficient in combination with the second reactive mean absolute deviation set, to obtain a reactive verification model corresponding to the updated low-through reactive model.

[0111] Further, in some embodiments, step S13 may be implemented by executing the following sub-steps S131 to S137:

[0112] Step S131: updating the reactive voltage reference coefficient based on a preset reactive voltage update amount.

[0113] Update reactive characteristic model parameters in the baseline model The update formula is: .in, It is After the update, the parameters is the number of updates, the initial value is Corresponding reactive voltage reference coefficient , is the update amount each time, usually .

[0114] Step S132: Calculate a first reactive mean absolute deviation update set during the fault period based on the updated reactive voltage coefficient.

[0115] Based on the updated , for the measured data set , calculate the corresponding reactive power mean absolute deviation set .

[0116] Step S133: When the maximum deviation in the first reactive mean absolute deviation update set is less than the preset reactive deviation, the reactive voltage coefficient is continuously updated until the maximum deviation obtained by the last update is greater than or equal to the preset reactive deviation, and the target reactive voltage coefficient is output.

[0117] set up is the active mean absolute deviation set The maximum deviation in .if , follow the above steps to continue updating ;if , then the updated parameters Recorded as .

[0118] Step S134: updating the reactive power recovery reference coefficient based on the preset reactive power recovery update amount.

[0119] Update the active characteristic model parameters in the baseline model The update formula is: .in, It is After the update, the parameters is the number of updates, the initial value is Corresponding reactive power recovery reference coefficient , is the update amount each time, usually .

[0120] Step S135: Calculate a second reactive mean absolute deviation update set during the post-fault restoration period based on the updated reactive restoration coefficient.

[0121] Based on the updated , for the measured data set , calculate the corresponding active mean absolute deviation set .

[0122] Step S136: When the maximum deviation in the second reactive mean absolute deviation update set is less than the preset reactive deviation, the reactive recovery coefficient is continuously updated until the maximum deviation obtained by the last update is greater than or equal to the preset reactive deviation, and the target reactive recovery coefficient is output.

[0123] set up is the active mean absolute deviation set The maximum deviation in .if , follow the above steps to continue updating ;if , then the updated parameters Recorded as .

[0124] Step S137: Substitute the target reactive voltage coefficient and the target reactive recovery coefficient into the updated low-breakthrough reactive model for parameter replacement to obtain a reactive verification model.

[0125] The parameters of the reactive characteristic model in the benchmark model are Updated to ,Will Updated to The updated model is called reactive power verification model.

[0126] Step S14: using the active power verification model and the reactive power verification model as verification models of the wind turbine generator set.

[0127] The active power verification model obtained in step S12 and the reactive power verification model obtained in step S13 are used as verification models reflecting the low penetration characteristics of the wind turbine generator system.

[0128] Step 103, performing equivalent evaluation based on a plurality of the reference models to obtain a low-through characteristic reference model of the wind farm, and performing equivalent evaluation based on a plurality of the verification models to obtain a low-through characteristic verification model of the wind farm;

[0129] For the equivalent treatment of wind farms, the low penetration characteristic model of wind turbines adopts the benchmark model, and then the wind turbines in the wind farm are equivalent to one unit by the aggregation method to obtain the low penetration characteristic benchmark model of the wind farm.

[0130] The wind turbine low penetration characteristic model adopts a verification model, and then the wind turbines in the wind farm are equivalent to one unit using the aggregation method to obtain the wind farm low penetration characteristic verification model.

[0131] In a specific implementation, multiple benchmark models are used to obtain a low penetration characteristic benchmark model of a wind farm, and multiple verification models are used to obtain a low penetration characteristic verification model of a wind farm, which can be:

[0132] The low penetration characteristic model of each wind turbine is set as a benchmark model to obtain multiple benchmark wind turbines, and the multiple benchmark wind turbines are converted into one unit through the aggregation method to obtain the low penetration characteristic benchmark model of the wind farm;

[0133] The low penetration characteristic model of each wind turbine is set as the verification model to obtain multiple verification wind turbines, and the multiple verification wind turbines are converted into one unit through the aggregation method to obtain the low penetration characteristic verification model of the wind farm.

[0134] Step 104: Use the low-throw characteristic verification model to perform an error check on the low-throw characteristic reference model. If the check passes, use the low-throw characteristic reference model for power grid security verification.

[0135] In the power grid simulation calculation, the baseline model of the wind farm's low penetration characteristics is first used to simulate the power grid safety check, and then the verification model is used for simulation calculation. If there is no significant difference in the simulation results of the two models, it means that the wind farm's low penetration characteristics baseline model is more accurate and can be used for power grid safety verification simulation. If there is a large difference in the simulation results, it means that the wind farm's low penetration characteristics baseline model is not accurate enough and needs to be further improved. The specific improvement method includes referring to the above process, re-conducting field tests and simulation modeling on the wind turbines, and then conducting safety verification.

[0136] In some embodiments, the low penetration characteristic verification model is used to perform error verification on the low penetration characteristic reference model. If the verification passes, the low penetration characteristic reference model is used for power grid security verification, which can be:

[0137] A simulation calculation of power grid safety verification is performed through a low-penetration characteristic benchmark model to obtain a benchmark simulation result; a verification simulation result is obtained by performing a simulation calculation of power grid safety verification through a low-penetration characteristic verification model; an error between the benchmark simulation result and the verification simulation result is calculated, and if the calculation error of the two is within a preset error range, it is determined that the calculation accuracy of the low-penetration characteristic benchmark model meets the requirements, and the low-penetration characteristic benchmark model is used for power grid safety verification.

[0138] In an embodiment of the present invention, a grid safety verification method for wind farm equivalence is provided. By constructing a wind turbine benchmark model, and obtaining a verification model based on parameter updates, combined with equivalent processing, a low-voltage ride-through characteristic benchmark model and a low-voltage ride-through characteristic verification model of the wind farm equivalent can be obtained. The equivalent verification model is used to perform an error check on the equivalent benchmark model, and it can be determined whether the equivalent benchmark model can be applied to grid safety verification. When the equivalent benchmark model that has passed the test is used for grid simulation calculation, a calculation result that meets the requirements of grid safety verification can be obtained. By adopting the technical solution of the present invention, the electromechanical transient model of the low voltage ride-through characteristics of the wind farm and the influence of parameter changes on the safety verification of the power system can be better handled, the equivalent accuracy of the wind farm can be improved, and the accuracy requirements of the safety verification of the power system can be met.

[0139] For better explanation, refer to Figure 3 , showing a schematic diagram of the overall process of a method for grid security verification of wind farm equivalent provided by an embodiment of the present invention. It should be pointed out that this embodiment only briefly describes the general process of grid security verification of wind farm equivalent, and the specific implementation process of each step can be understood by referring to the relevant content in the aforementioned embodiment, which will not be described here. It can be understood that the present invention is not limited to this.

[0140] Step 301: Obtain low-power wear characteristic data of each wind turbine generator set under different short-circuit fault conditions in the power system.

[0141] Step 302: construct a low-voltage active model and a low-voltage reactive model of the wind turbine generator set.

[0142] Step 303: setting the low-through simulation data, taking the minimum deviation between the low-through simulation data and the low-through characteristic data as the goal, using the least squares method to solve the low-through active model and the low-through reactive model respectively, and using the solved active characteristic model parameters as the active benchmark model parameters to update the low-through active model, and using the solved reactive characteristic model parameters as the reactive benchmark model parameters to update the low-through reactive model; obtaining the benchmark model of the wind turbine.

[0143] Step 304: Calculate a first active average absolute deviation set and a first reactive average absolute deviation set during the fault period, and a second active average absolute deviation set and a second reactive average absolute deviation set during the post-fault recovery period according to the low-throughput characteristic data.

[0144] Step 305: Update the parameters of the active benchmark model in combination with the first active average absolute deviation set and the second active average absolute deviation set to obtain an active verification model; update the parameters of the reactive benchmark model in combination with the first reactive average absolute deviation set and the second reactive average absolute deviation set to obtain a reactive verification model; obtain the verification model of the wind turbine.

[0145] Step 306: Equalization is performed based on multiple benchmark models to obtain a low-through characteristic benchmark model of the wind farm, and equalization is performed based on multiple verification models to obtain a low-through characteristic verification model of the wind farm.

[0146] Step 307: Perform simulation calculations for power grid security verification using the low penetration characteristic benchmark model to obtain benchmark simulation results; perform simulation calculations for power grid security verification using the low penetration characteristic verification model to obtain verification simulation results.

[0147] Step 308: Calculate the error between the benchmark simulation result and the verification simulation result. If the calculated error between the two is within the preset error range, it is determined that the calculation accuracy of the low-penetration characteristic benchmark model meets the requirements, and the low-penetration characteristic benchmark model is used for power grid security verification.

[0148] Reference Figure 4 , shows a structural block diagram of a power grid security verification device for wind farm equivalent provided by an embodiment of the present invention, the device may include:

[0149] The data acquisition module 401 is used to acquire the low-power wear characteristic data of each wind turbine generator set under different short-circuit fault conditions in the power system;

[0150] A model building module 402 is used to build a benchmark model of each wind turbine generator set according to the low penetration characteristic data, and update the parameters of the benchmark model based on the maximum allowable value of the mean absolute deviation to obtain a verification model;

[0151] The equivalent processing module 403 is used to perform equivalent processing based on the multiple reference models to obtain a low-through characteristic reference model of the wind farm, and perform equivalent processing based on the multiple verification models to obtain a low-through characteristic verification model of the wind farm;

[0152] The verification module 404 is used to use the low-throttle characteristic verification model to perform an error verification on the low-throttle characteristic reference model. If the verification passes, the low-throttle characteristic reference model is used for power grid security verification.

[0153] In an optional embodiment, the model building module 402 includes:

[0154] A low-through characteristic model construction module is used to construct a low-through characteristic model of the wind turbine generator set; the low-through characteristic model includes a low-through active model and a low-through reactive model; the low-through active model is used to describe the low-through active characteristic of the wind turbine generator set, and the low-through reactive model is used to describe the low-through reactive characteristic of the wind turbine generator set;

[0155] A model updating module is used to set low-power wear simulation data, with the goal of minimizing the deviation between the low-power wear simulation data and the low-power wear characteristic data, respectively solve the low-power wear active model and the low-power wear reactive model using the least square method, and use the solved active characteristic model parameters as active reference model parameters to update the low-power wear active model, and use the solved reactive characteristic model parameters as reactive reference model parameters to update the low-power wear reactive model;

[0156] The benchmark model definition module is used to use the updated low-power active model and the updated low-power reactive model as the benchmark models of the wind turbine generator set.

[0157] In an optional embodiment, the active reference model parameters include an active voltage reference coefficient and an active recovery reference coefficient, the reactive reference model parameters include a reactive voltage reference coefficient and a reactive recovery reference coefficient, and the model construction module 402 includes:

[0158] A mean absolute deviation set data calculation module, used to calculate a first active mean absolute deviation set and a first reactive mean absolute deviation set during a fault period, and a second active mean absolute deviation set and a second reactive mean absolute deviation set during a post-fault recovery period according to the low-throughput characteristic data;

[0159] An active parameter updating module, used to update the active voltage reference coefficient in combination with the first active mean absolute deviation set, and to update the active recovery reference coefficient in combination with the second active mean absolute deviation set, to obtain an active verification model corresponding to the updated low-breakthrough active model;

[0160] A reactive parameter updating module, used to update the reactive voltage reference coefficient in combination with the first reactive mean absolute deviation set, and to update the reactive recovery reference coefficient in combination with the second reactive mean absolute deviation set, to obtain a reactive verification model corresponding to the updated low-through reactive model;

[0161] The calibration model definition module is used to use the active power calibration model and the reactive power calibration model as calibration models of the wind turbine generator set.

[0162] In an optional embodiment, the active parameter updating module includes:

[0163] An active voltage reference coefficient updating module, used for updating the active voltage reference coefficient based on a preset active voltage update amount;

[0164] A first deviation set data updating module, configured to calculate a first active mean absolute deviation update set during a fault period based on an updated active voltage coefficient;

[0165] An active voltage coefficient iterative updating module is used to, when the maximum deviation in the first active mean absolute deviation update set is less than a preset active deviation, continue to update the active voltage coefficient until the maximum deviation obtained by the last update is greater than or equal to the preset active deviation, stop updating, and output a target active voltage coefficient;

[0166] An active power recovery reference coefficient updating module, used for updating the active power recovery reference coefficient based on a preset active power recovery update amount;

[0167] A second deviation set data updating module, configured to calculate a second active mean absolute deviation update set during a post-fault recovery period based on an updated active recovery coefficient;

[0168] An active power recovery coefficient iterative updating module is used to, when the maximum deviation in the second active power mean absolute deviation update set is less than a preset active power deviation, continue to update the active power recovery coefficient until the maximum deviation obtained by the last update is greater than or equal to the preset active power deviation, stop updating, and output a target active power recovery coefficient;

[0169] The active power verification model construction module is used to substitute the target active power voltage coefficient and the target active power recovery coefficient into the updated low-breakthrough active power model for parameter replacement to obtain the active power verification model.

[0170] In an optional embodiment, the reactive parameter updating module includes:

[0171] A reactive voltage reference coefficient updating module, used for updating the reactive voltage reference coefficient based on a preset reactive voltage update amount;

[0172] A third deviation set data updating module, configured to calculate a first reactive average absolute deviation update set during a fault period based on an updated reactive voltage coefficient;

[0173] A reactive voltage coefficient iterative updating module is used to, when the maximum deviation in the first reactive mean absolute deviation update set is less than a preset reactive deviation, continue to update the reactive voltage coefficient until the maximum deviation obtained by the last update is greater than or equal to the preset reactive deviation, stop updating, and output a target reactive voltage coefficient;

[0174] A reactive power recovery reference coefficient updating module, used for updating the parameters of the reactive power recovery reference coefficient based on a preset reactive power recovery update amount;

[0175] a fourth deviation set data updating module, configured to calculate a second reactive average absolute deviation update set during a post-fault recovery period based on the updated reactive recovery coefficient;

[0176] A reactive power recovery coefficient iterative updating module is used to, when the maximum deviation in the second reactive mean absolute deviation update set is less than a preset reactive power deviation, continue to update the reactive power recovery coefficient until the maximum deviation obtained by the last update is greater than or equal to the preset reactive power deviation, stop updating, and output a target reactive power recovery coefficient;

[0177] The reactive power verification model construction module is used to substitute the target reactive power voltage coefficient and the target reactive power recovery coefficient into the updated low-breakthrough reactive power model for parameter replacement to obtain a reactive power verification model.

[0178] In an optional embodiment, the equal value processing module 403 includes:

[0179] A reference wind turbine generator set equivalent module is used to set the low penetration characteristic model of each wind turbine generator set as a reference model, obtain multiple reference wind turbine generator sets, and use an aggregation method to equate the multiple reference wind turbine generator sets into one set to obtain a low penetration characteristic reference model of the wind farm;

[0180] The wind turbine group verification equivalence module is used to set the low penetration characteristic model of each wind turbine group as a verification model, obtain multiple verification wind turbine groups, and use the aggregation method to equate the multiple verification wind turbine groups into one unit to obtain the low penetration characteristic verification model of the wind farm.

[0181] In an optional embodiment, the inspection module 404 includes:

[0182] A benchmark simulation calculation module, used to perform simulation calculation of power grid safety verification through the low-throughput characteristic benchmark model to obtain a benchmark simulation result;

[0183] A verification simulation calculation module is used to perform simulation calculation of power grid safety verification through the low penetration characteristic verification model to obtain verification simulation results;

[0184] The error judgment module is used to calculate the error between the benchmark simulation result and the verification simulation result. If the calculated error between the two is within a preset error range, it is determined that the calculation accuracy of the low-penetration characteristic benchmark model meets the requirements, and the low-penetration characteristic benchmark model is used for power grid safety verification.

[0185] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the aforementioned method embodiment.

[0186] It should be noted that, in order to enable those skilled in the art to better distinguish data of the same type but with different actual meanings, the embodiments of the present invention use first, second and other descriptive nouns to distinguish and explain some technical features. The first, second and other descriptive nouns are only used to distinguish data and have no other special meanings. It can be understood that the present invention is not limited to this.

[0187] An embodiment of the present invention further provides an electronic device, the device comprising a processor and a memory:

[0188] The memory is used to store the program code and transmit the program code to the processor;

[0189] The processor is used to execute the power grid security verification method for wind farm equivalence according to any embodiment of the present invention according to the instructions in the program code.

[0190] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the power grid security verification method for wind farm equivalence according to any embodiment of the present invention.

[0191] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0192] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0193] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0194] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0195] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0196] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for checking the safety of a power grid for wind farm equivalence, characterized in that: The wind farm includes a plurality of wind turbines, and the method includes: Acquiring low-throttle characteristic data of each wind turbine generator set under different short-circuit fault conditions in the power system; For each wind turbine generator set, a benchmark model of the wind turbine generator set is constructed according to the low penetration characteristic data, and parameters of the benchmark model are updated based on a maximum allowable mean absolute deviation to obtain a verification model; Performing equalization based on a plurality of the reference models to obtain a low penetration characteristic reference model of the wind farm, and performing equalization based on a plurality of the verification models to obtain a low penetration characteristic verification model of the wind farm; The low-throat characteristic verification model is used to perform an error check on the low-throat characteristic reference model. If the check passes, the low-throat characteristic reference model is used for power grid safety verification.

2. The power grid security verification method for wind farm equivalent according to claim 1 is characterized in that: The step of constructing a benchmark model of the wind turbine generator system according to the low penetration characteristic data includes: Constructing a low-power-through characteristic model of the wind turbine generator set; the low-power-through characteristic model includes a low-power-through active model and a low-power-through reactive model; the low-power-through active model is used to describe the low-power-through active characteristic of the wind turbine generator set, and the low-power-through reactive model is used to describe the low-power-through reactive characteristic of the wind turbine generator set; With the goal of minimizing the deviation between the set low-through simulation data and the low-through characteristic data, the least square method is used to solve the low-through active model and the low-through reactive model respectively, and the solved active characteristic model parameters are used as active reference model parameters to update the low-through active model, and the solved reactive characteristic model parameters are used as reactive reference model parameters to update the low-through reactive model; The updated low-voltage active model and the updated low-voltage reactive model are used as the benchmark models of the wind turbine generator set.

3. The power grid security verification method for wind farm equivalent according to claim 2 is characterized in that: The active reference model parameters include an active voltage reference coefficient and an active recovery reference coefficient, and the reactive reference model parameters include a reactive voltage reference coefficient and a reactive recovery reference coefficient; The step of updating the parameters of the benchmark model based on the maximum allowable value of the mean absolute deviation to obtain a verification model includes: Calculating, according to the low-throughput characteristic data, a first active average absolute deviation set and a first reactive average absolute deviation set during a fault period, and a second active average absolute deviation set and a second reactive average absolute deviation set during a post-fault recovery period; In combination with the first active mean absolute deviation set, the active voltage reference coefficient is updated with parameters, and in combination with the second active mean absolute deviation set, the active recovery reference coefficient is updated with parameters to obtain an active verification model corresponding to the updated low-throughput active model; The reactive voltage reference coefficient is updated in combination with the first reactive mean absolute deviation set, and the reactive recovery reference coefficient is updated in combination with the second reactive mean absolute deviation set to obtain a reactive verification model corresponding to the updated low-through reactive model; The active power verification model and the reactive power verification model are used as verification models of the wind turbine generator set.

4. The power grid security verification method for wind farm equivalent according to claim 3 is characterized in that: The step of updating the active voltage reference coefficient in combination with the first active mean absolute deviation set, and updating the active recovery reference coefficient in combination with the second active mean absolute deviation set, to obtain an active verification model corresponding to the updated low-throughput active model, includes: Performing parameter updating on the active voltage reference coefficient based on a preset active voltage update amount; Calculating a first active mean absolute deviation update set during the fault period based on the updated active voltage coefficient; When the maximum deviation in the first active mean absolute deviation update set is less than the preset active deviation, the active voltage coefficient continues to be updated until the maximum deviation obtained by the last update is greater than or equal to the preset active deviation, and the target active voltage coefficient is output; Performing parameter updating on the active power recovery reference coefficient based on a preset active power recovery update amount; Calculating a second active mean absolute deviation update set during a post-fault recovery period based on the updated active recovery coefficient; When the maximum deviation in the second active mean absolute deviation update set is less than the preset active deviation, continue to update the active recovery coefficient until the maximum deviation obtained by the last update is greater than or equal to the preset active deviation, and output the target active recovery coefficient; The target active voltage coefficient and the target active recovery coefficient are substituted into the updated low-breakdown active model for parameter replacement to obtain an active verification model.

5. The power grid security verification method for wind farm equivalent according to claim 3 is characterized in that: The method of updating the parameters of the reactive voltage reference coefficient in combination with the first reactive mean absolute deviation set, and updating the parameters of the reactive recovery reference coefficient in combination with the second reactive mean absolute deviation set, to obtain a reactive verification model corresponding to the updated low-through reactive model, includes: Performing parameter updating on the reactive voltage reference coefficient based on a preset reactive voltage update amount; Calculate a first reactive mean absolute deviation update set during the fault period based on the updated reactive voltage coefficient; When the maximum deviation in the first reactive average absolute deviation update set is less than the preset reactive deviation, the reactive voltage coefficient is continuously updated until the maximum deviation obtained by the last update is greater than or equal to the preset reactive deviation, and the target reactive voltage coefficient is output; Performing parameter updating on the reactive power recovery reference coefficient based on a preset reactive power recovery update amount; calculating a second reactive mean absolute deviation update set during a post-fault restoration period based on the updated reactive restoration coefficient; When the maximum deviation in the second reactive mean absolute deviation update set is less than the preset reactive deviation, the reactive recovery coefficient is continuously updated until the maximum deviation obtained by the last update is greater than or equal to the preset reactive deviation, and the target reactive recovery coefficient is output; The target reactive voltage coefficient and the target reactive recovery coefficient are substituted into the updated low-breakthrough reactive model for parameter replacement to obtain a reactive verification model.

6. The power grid security verification method for wind farm equivalent according to any one of claims 1 to 5, characterized in that: The step of performing equalization based on a plurality of the reference models to obtain a low penetration characteristic reference model of the wind farm, and performing equalization based on a plurality of the verification models to obtain a low penetration characteristic verification model of the wind farm, comprises: The low penetration characteristic model of each wind turbine set is set as a benchmark model to obtain a plurality of benchmark wind turbine sets, and the plurality of benchmark wind turbine sets are converted into one set by an aggregation method to obtain a low penetration characteristic benchmark model of the wind farm; The low penetration characteristic model of each wind turbine is set as a verification model to obtain multiple verification wind turbines, and the multiple verification wind turbines are combined into one unit through aggregation method to obtain the low penetration characteristic verification model of the wind farm.

7. The power grid security verification method for wind farm equivalent according to claim 6 is characterized in that: The adopting the low-throw characteristic verification model to perform an error check on the low-throw characteristic reference model, and if the check passes, using the low-throw characteristic reference model for power grid security verification, comprises: Performing simulation calculations for power grid safety verification using the low penetration characteristic benchmark model to obtain benchmark simulation results; Performing simulation calculation of power grid safety verification through the low penetration characteristic verification model to obtain verification simulation results; The error between the benchmark simulation result and the verification simulation result is calculated, and if the error is within a preset error range, the low penetration characteristic benchmark model is used for power grid security verification.

8. A power grid security verification device for wind farm equivalent, characterized in that: The wind farm includes a plurality of wind turbines, and the device includes: A data acquisition module, used to acquire low-throttle characteristic data of each wind turbine generator set under different short-circuit fault conditions in the power system; A model building module, for building a benchmark model of each wind turbine generator set according to the low penetration characteristic data, and updating the parameters of the benchmark model based on the maximum allowable value of the mean absolute deviation to obtain a verification model; An equivalent processing module, used to perform equivalent processing based on a plurality of the reference models to obtain a low-through characteristic reference model of the wind farm, and perform equivalent processing based on a plurality of the verification models to obtain a low-through characteristic verification model of the wind farm; The verification module is used to use the low-throttle characteristic verification model to perform error verification on the low-throttle characteristic reference model. If the verification passes, the low-throttle characteristic reference model is used for power grid safety verification.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power grid security verification method for wind farm equivalence according to any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the power grid security verification method for wind farm equivalence according to any one of claims 1 to 7.

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