Low-pass characteristic parameter setting method and device suitable for wind power plant equivalence

Abstract

The invention discloses a low-voltage-crossing characteristic parameter setting method and a low-voltage-crossing characteristic parameter setting device suitable for wind power plant equivalence, which are used for solving the problem of how to improve modeling efficiency in electromechanical transient and parameter setting verification of low-voltage-crossing characteristics of a wind turbine generator. The method comprises the following steps: constructing an actual measurement data sequence and a simulation data sequence of the wind turbine generator set for low-penetration characteristic modeling; according to the actual measurement data sequence and the simulation data sequence, calculating a total mean absolute error under different verification working conditions; constructing a low-voltage penetration characteristic model of the wind turbine generator; the low-penetration characteristic model corresponds to a to-be-solved low-penetration characteristic parameter; and taking the minimum total mean absolute error as an objective function, and performing particle swarm optimization solution on the low-pass characteristic parameters to obtain the set low-pass characteristic parameters. Therefore, the total mean absolute error under different verification working conditions is calculated in combination with actual measurement and simulation, the low-pass characteristic parameters are further optimized and solved, the low-pass characteristic parameter setting problem in the wind power plant equivalence process can be solved, and the wind power plant equivalence modeling requirement is met.

Description

Technical Field

[0001] The present invention relates to the field of power system equivalent technology, and in particular to a method for setting low-throughput characteristic parameters applicable to wind farm equivalents, a device for setting low-throughput characteristic parameters applicable to wind farm equivalents, 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 (hereinafter referred to as 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 characteristic parameters of wind turbines in wind farms are not set accurately, there will be a large deviation between the electromechanical transient characteristics of the power system simulation 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-through characteristics of wind turbines in wind farms generally first obtains the low-through characteristic curve of the wind turbine through actual measurement, and then obtains the low-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-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-through characteristics of wind turbines that meet the verification result evaluation requirements can be used for electromechanical transient simulation calculations of power systems.

[0004] However, since the verification method involves many verification conditions, a large amount of measured data, and cumbersome verification steps and result evaluation, in order to improve the modeling efficiency, it is of practical significance to propose a low-throughput characteristic parameter setting method suitable for wind farms. Summary of the invention

[0005] The present invention provides a method for setting parameters of low-through characteristics of wind farms, a device for setting parameters of low-through characteristics of wind farms, an electronic device and a storage medium, which are used to solve or partially solve the technical problem of how to improve the modeling efficiency in the electromechanical transient and parameter setting verification of the low-through characteristics of wind turbines.

[0006] The present invention provides a method for setting low penetration characteristic parameters applicable to wind farms, including:

[0007] Construct measured data sequences and simulation data sequences for wind turbines to model low penetration characteristics;

[0008] Calculating the total mean absolute error under different verification conditions according to the measured data sequence and the simulation data sequence;

[0009] Constructing a low penetration characteristic model of the wind turbine generator set; the low penetration characteristic model corresponds to the low penetration characteristic parameters to be solved;

[0010] Taking the minimum total mean absolute error as the objective function, the low penetration characteristic parameters are solved by particle swarm optimization to obtain the adjusted low penetration characteristic parameters.

[0011] Optionally, the measured data sequence includes a voltage measured sequence, an active power measured sequence, and a reactive power measured sequence; the simulation data sequence includes a voltage simulation sequence, an active power simulation sequence, and a reactive power simulation sequence; and the total mean absolute error under different verification conditions is calculated according to the measured data sequence and the simulation data sequence, including:

[0012] Identify the starting measured data and the measured reference point of the voltage value suddenly dropping downward when the fault starts in the voltage measured sequence, and identify the starting simulation data and the simulation reference point of the voltage value suddenly dropping downward when the fault starts in the voltage simulation sequence;

[0013] According to the measured reference point and the simulated reference point, the measured active power sequence and the simulated active power sequence are aligned to obtain an active power alignment sequence, and the measured reactive power sequence and the simulated reactive power sequence are aligned to obtain a reactive power alignment sequence;

[0014] For each verification working condition, the active mean absolute error is calculated according to the active alignment sequence, and the reactive mean absolute error is calculated according to the reactive alignment sequence;

[0015] Calculate the total average absolute error of active power under different verification conditions according to each of the average absolute errors of active power;

[0016] Calculate the total reactive power average absolute error under different verification conditions according to each reactive power average absolute error;

[0017] The total average absolute error of active power and the total average absolute error of reactive power are taken as the total average absolute error of the wind turbine generator set under different verification conditions.

[0018] Optionally, the method further comprises:

[0019] When the active power measured sequence and the active power simulation sequence are aligned based on the measured reference point and the simulation reference point, or when the reactive power measured sequence and the reactive power simulation sequence are aligned, it is assumed that the measured data sequence has a first number of data on the left side of the measured reference point and a second number of data on the right side; the simulation data sequence has a third number of data on the left side of the simulation reference point and a fourth number of data on the right side;

[0020] If the first number is greater than the third number, the third number is used as the number of data located on the left side of the reference point in the sequence obtained after position alignment;

[0021] If the first number is smaller than the third number, the first number is used as the number of data located on the left side of the reference point in the sequence obtained after position alignment;

[0022] If the second number is greater than the fourth number, the fourth number is used as the number of data located on the right side of the reference point in the sequence obtained after position alignment;

[0023] If the second number is smaller than the fourth number, the second number is used as the number of data located on the right side of the reference point in the sequence obtained after the position alignment.

[0024] Optionally, the low-through characteristic model includes a low-through active characteristic model and a low-through reactive characteristic model; the low-through characteristic parameters to be solved include active characteristic parameters of the low-through active characteristic model and reactive characteristic parameters of the low-through reactive characteristic model; the method of performing particle swarm optimization on the low-through characteristic parameters with the minimum total mean absolute error as the objective function to obtain the set low-through characteristic parameters includes:

[0025] Taking the minimum total active mean absolute error as the first objective function and the active characteristic parameter as the first optimization solution item, a particle swarm optimization algorithm is used to optimize and solve the active characteristic parameter to obtain the set active characteristic parameter;

[0026] The minimum total reactive power average absolute error is taken as the second objective function, the reactive characteristic parameter is taken as the second optimization solution item, and the reactive characteristic parameter is optimized and solved by using a particle swarm optimization algorithm to obtain the set reactive characteristic parameter.

[0027] Optionally, the active characteristic parameters include an active voltage coefficient, an active current coefficient, an active current setting value, an active recovery coefficient, and an active recovery value, and the particle swarm optimization algorithm is used to optimize and solve the active characteristic parameters to obtain the set active characteristic parameters, including:

[0028] Taking the minimum total active average absolute error as the first fitness function;

[0029] The active voltage coefficient, the active current coefficient, the active current setting value, the active recovery coefficient and the active recovery value are used as the position information of the particle and encoded;

[0030] The active voltage coefficient change, the active current coefficient change, the active current setting value change, the active recovery coefficient change and the active recovery value change are used as the particle speed and encoded;

[0031] The initial solution of active voltage coefficient, the initial solution of active current coefficient, the initial solution of active current setting value, the initial solution of active recovery coefficient and the initial solution of active recovery value are obtained by heuristic method as the initial generation particles of the first particle group;

[0032] Iteratively optimizing and solving the first generation particles of the first particle swarm in combination with the first fitness function until a maximum number of iterations is reached, and outputting a first optimal particle with the smallest fitness function value in the last generation particle swarm, wherein the first optimal particle corresponds to an optimal active voltage coefficient, an optimal active current coefficient, an optimal active current setting value, an optimal active recovery coefficient, and an optimal active recovery value;

[0033] The optimal active voltage coefficient, the optimal active current coefficient, the optimal active current setting value, the optimal active recovery coefficient and the optimal active recovery value are used as the adjusted active characteristic parameters of the low-power active characteristic model.

[0034] Optionally, the reactive characteristic parameters include a reactive voltage coefficient, a reactive current coefficient, a reactive current setting value, a reactive recovery coefficient, and a reactive recovery value, and the particle swarm optimization algorithm is used to optimize and solve the reactive characteristic parameters to obtain the set reactive characteristic parameters, including:

[0035] Taking the minimum total reactive power average absolute error as the second fitness function;

[0036] The reactive voltage coefficient, the reactive current coefficient, the reactive current setting value, the reactive recovery coefficient and the reactive recovery value are used as the position information of the particle and encoded;

[0037] The reactive voltage coefficient change, the reactive current coefficient change, the reactive current setting value change, the reactive recovery coefficient change and the reactive recovery value change are used as the particle speed and encoded;

[0038] The initial solutions of reactive voltage coefficient, reactive current coefficient, reactive current setting value, reactive recovery coefficient and reactive recovery value are obtained by heuristic method as the first generation particles of the second particle swarm;

[0039] In combination with the second fitness function, the first generation particles of the second particle swarm are iteratively optimized and solved until the maximum number of iterations is reached, and the second optimal particle with the smallest fitness function value in the last generation of particle swarm is output, and the second optimal particle corresponds to the optimal reactive voltage coefficient, the optimal reactive current coefficient, the optimal reactive current setting value, the optimal reactive recovery coefficient and the optimal reactive recovery value;

[0040] The optimal reactive voltage coefficient, the optimal reactive current coefficient, the optimal reactive current setting value, the optimal reactive recovery coefficient and the optimal reactive recovery value are used as the adjusted reactive characteristic parameters of the low-throughput reactive characteristic model.

[0041] Optionally, the wind turbine generator system corresponds to a plurality of verification operating conditions, and each verification operating condition corresponds to a set of measured data consisting of three-phase instantaneous measurement values ​​of voltage and three-phase instantaneous measurement values ​​of current; the step of constructing a measured data sequence and a simulation data sequence for low-through characteristic modeling of the wind turbine generator system includes:

[0042] Determine the basic control parameters required for low-through characteristic modeling and parameter setting of the wind turbine generator set; the basic control parameters include a capacity base value, a voltage reference value, a fundamental wave period value, a simulation calculation step size, and a simulation calculation duration of simulation calculation;

[0043] For each of the verification conditions, according to the basic control parameters, the three-phase instantaneous measurement values ​​of the voltage, and the three-phase instantaneous measurement values ​​of the current, calculate the voltage fundamental positive sequence component, the active power fundamental positive sequence component, and the reactive power fundamental positive sequence component under the verification condition;

[0044] Calculate the sampling interval and the total amount of the measured data by combining the sampling interval of the measured data, the simulation calculation step length and the simulation calculation time length;

[0045] According to the sampling interval and the total amount of sampling, the voltage fundamental positive sequence component, the active power fundamental positive sequence component and the reactive power fundamental positive sequence component are sampled at intervals to obtain a voltage measured sequence, an active power measured sequence and a reactive power measured sequence as a measured data sequence for low-throughput characteristic modeling of the wind turbine generator set;

[0046] Constructing a single-machine infinite system of the wind turbine generator set;

[0047] For each of the verification conditions, the initial operating conditions of the wind turbine are set, and fault simulation is performed in combination with the basic control parameters and the voltage drop depth in the verification conditions to obtain a voltage simulation sequence, an active power simulation sequence, and a reactive power simulation sequence as simulation data sequences for low-thru characteristic modeling of the wind turbine.

[0048] The present invention also provides a low penetration characteristic parameter setting device applicable to a wind farm, comprising:

[0049] A data sequence construction module is used to construct measured data sequences and simulation data sequences for low penetration characteristic modeling of wind turbines;

[0050] A total mean absolute error calculation module, used to calculate the total mean absolute error under different verification conditions according to the measured data sequence and the simulation data sequence;

[0051] A low penetration characteristic model building module is used to build a low penetration characteristic model of the wind turbine generator set; the low penetration characteristic model corresponds to the low penetration characteristic parameters to be solved;

[0052] The parameter setting module is used to use the minimum total mean absolute error as the objective function, perform particle swarm optimization on the low penetration characteristic parameters, and obtain the low penetration characteristic parameters after setting.

[0053] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0054] The memory is used to store program code and transmit the program code to the processor;

[0055] The processor is used to execute the low-throughput characteristic parameter setting method applicable to wind farm equivalent values ​​as described in any of the above items according to the instructions in the program code.

[0056] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program codes, wherein the program codes are used to execute the method for setting low-throughput characteristic parameters applicable to wind farm equivalents as described in any one of the above items.

[0057] It can be seen from the above technical solutions that the present invention has the following advantages:

[0058] A method for setting low-through characteristic parameters suitable for wind farm equivalence is provided. First, the measured data sequence and simulation data sequence of the wind turbine for low-through characteristic modeling are constructed; then, the total mean absolute error under different verification conditions is calculated based on the measured data sequence and the simulation data sequence; then, the low-through characteristic model of the wind turbine is constructed; the low-through characteristic model corresponds to the low-through characteristic parameters to be solved; finally, the particle swarm optimization is performed on the low-through characteristic parameters with the minimum total mean absolute error as the objective function to obtain the set low-through characteristic parameters. Thus, the total mean absolute error under different verification conditions is calculated by combining the measured and simulated calculations, and the low-through characteristic parameters are further optimized and solved, which can handle the problem of setting low-through characteristic parameters in the process of wind farm equivalence and meet the needs of wind farm equivalence modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] 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.

[0060] Figure 1 A flow chart of the steps of a method for setting low penetration characteristic parameters applicable to wind farms;

[0061] Figure 2 (a) is a schematic diagram of the active power characteristic curve after a wind farm fault occurs;

[0062] Figure 2 (b) is a schematic diagram of the reactive power characteristic curve after a wind farm fault occurs;

[0063] Figure 3 It is a schematic diagram of the overall process of a method for setting low penetration characteristic parameters applicable to wind farms;

[0064] Figure 4 The figure is a structural block diagram of a low penetration characteristic parameter setting device suitable for wind farms. DETAILED DESCRIPTION

[0065] The embodiments of the present invention provide a method for setting low-through characteristic parameters applicable to wind farms, a device for setting low-through characteristic parameters applicable to wind farms, an electronic device and a storage medium, which are used to solve or partially solve the technical problem of how to improve the modeling efficiency in the electromechanical transient and parameter setting verification of the low-through characteristic of a wind turbine.

[0066] 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.

[0067] 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-through characteristics of wind turbines in the wind farm generally first obtains the low-through characteristic curve of the wind turbine through actual measurement, and then obtains the low-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-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-through characteristics of wind turbines that meet the verification result evaluation requirements can be used for electromechanical transient simulation calculations of power systems.

[0068] However, since the verification method involves many verification conditions, a large amount of measured data, and cumbersome verification steps and result evaluation, in order to improve the modeling efficiency, it is of practical significance to propose a low-throughput characteristic parameter setting method suitable for wind farms.

[0069] Therefore, one of the core inventive points of the embodiment of the present invention is: in view of the deficiencies in the prior art, a method for setting low-through characteristic parameters suitable for wind farm equivalence is proposed. First, a measured data sequence and a simulation data sequence for low-through characteristic modeling of the wind turbine are constructed; then, the total mean absolute error under different verification conditions is calculated based on the measured data sequence and the simulation data sequence; then, a low-through characteristic model of the wind turbine is constructed; the low-through characteristic model corresponds to the low-through characteristic parameters to be solved; finally, the particle swarm optimization is performed on the low-through characteristic parameters with the minimum total mean absolute error as the objective function to obtain the adjusted low-through characteristic parameters. Thus, the total mean absolute error under different verification conditions is calculated by combining the measured and simulated calculations, and the minimum total mean absolute error is used as the objective function. Based on the particle swarm optimization algorithm, the low-through characteristic parameters to be solved in the low-through characteristic model of the wind turbine are further optimized and solved, which can handle the problem of setting the low-through characteristic parameters in the process of wind farm equivalence and meet the requirements of wind farm equivalence modeling.

[0070] Reference Figure 1 , shows a flowchart of a method for setting low-through characteristic parameters of wind farm equivalent values ​​provided by an embodiment of the present invention, which may specifically include the following steps:

[0071] Step 101, constructing a measured data sequence and a simulation data sequence for low-throughput characteristic modeling of a wind turbine generator set;

[0072] In this step, the main purpose is to obtain the measured data sequence and simulation data sequence of the wind turbine generator set for subsequent low-penetration characteristic modeling.

[0073] Assume that there are N verification conditions for the wind turbine (i.e., the wind turbine can correspond to multiple verification conditions). Each verification condition corresponds to a set of measured data consisting of three-phase instantaneous measurement values ​​of voltage and three-phase instantaneous measurement values ​​of current.

[0074] In a specific implementation, the process of constructing a measured data sequence and a simulation data sequence for low-throughput characteristic modeling of a wind turbine generator set may include the following sub-steps S01 to S06:

[0075] Step S01: determining the basic control parameters required for low-through characteristic modeling and parameter setting of the wind turbine; the basic control parameters include the capacity base value, voltage reference value, fundamental wave period value, simulation calculation step length and simulation calculation time of simulation calculation;

[0076] The basic control parameters for low penetration characteristic modeling and parameter setting are determined based on wind turbine parameters and measured data. They can mainly include: capacity base value, voltage reference value, fundamental wave period value, simulation calculation step size and simulation calculation duration. Among them, the fundamental wave period value is generally set to 0.02 seconds, the simulation calculation step size is generally set to 0.002 seconds, and the simulation calculation duration is generally set to 10 to 50 seconds.

[0077] Step S02: for each verification condition, according to the basic control parameters, the instantaneous measurement values ​​of the three-phase voltage and the instantaneous measurement values ​​of the three-phase current, the voltage fundamental positive sequence component, the active power fundamental positive sequence component and the reactive power fundamental positive sequence component under the verification condition are calculated;

[0078] For a certain verification condition, the voltage fundamental positive sequence component under the verification condition can be calculated based on the basic control parameters and the measured data of the corresponding three-phase instantaneous measurement values ​​of voltage and current. , active power fundamental positive sequence component and reactive power fundamental positive sequence component :

[0079]

[0080]

[0081]

[0082] in, , , The various parameters involved can be specifically expressed as:

[0083]

[0084]

[0085] ,

[0086] ,

[0087] ,

[0088]

[0089]

[0090] ,

[0091] ,

[0092] ,

[0093] In the formula, , , Respectively represent the instantaneous measured values ​​of phase a, b, and c voltages; , , Respectively represent the instantaneous measurement values ​​of phase a, b, and c currents; It represents the fundamental wave period value; Represents the cosine component of the voltage fundamental positive sequence component; Represents the sinusoidal component of the voltage fundamental positive sequence component; Represents the cosine component of the fundamental positive sequence component of the current; Represents the sinusoidal component of the fundamental positive sequence component of the current; Represents the cosine component of the voltage measurement value of phase a; Represents the cosine component of the voltage measurement value of phase a; Represents the cosine component of the b-phase voltage measurement value; Represents the cosine component of the b-phase voltage measurement value; Represents the cosine component of the c-phase voltage measurement; Represents the cosine component of the c-phase voltage measurement; Represents the cosine component of the a-phase current measurement value; Represents the cosine component of the a-phase current measurement value; Represents the cosine component of the b-phase current measurement value; Represents the cosine component of the b-phase current measurement value; Represents the cosine component of the c-phase current measurement value; Represents the cosine component of the c-phase current measurement.

[0094] Step S03: Calculate the sampling interval and the total amount of the measured data in combination with the sampling interval of the measured data, the simulation calculation step length and the simulation calculation time;

[0095] According to the sampling interval of the measured data and the simulation calculation step size, the sampling interval of the measured data can be calculated as: .in, Indicates that the calculation result is rounded to an integer.

[0096] According to the simulation calculation step size and simulation calculation time, the total amount of measured data extracted can be calculated as .

[0097] Step S04: according to the sampling interval and the total amount of sampling, the voltage fundamental positive sequence component, the active power fundamental positive sequence component and the reactive power fundamental positive sequence component are sampled at intervals to obtain the voltage measured sequence, the active power measured sequence and the reactive power measured sequence as the measured data sequence for low-through characteristic modeling of the wind turbine;

[0098] According to the sampling interval and total amount of the measured data, the fundamental positive sequence component of the active power and the fundamental positive sequence component of reactive power Extract data from the positive sequence component of the voltage fundamental wave at the same time. After the interval sampling is completed, the voltage measured data sequence, active power measured data sequence and reactive power measured data sequence for wind turbine low-thru characteristic modeling can be formed.

[0099] Among them, in Under the following verification conditions:

[0100] The voltage measured data sequence can be expressed as: ;

[0101] The active power measurement sequence can be expressed as: ;

[0102] The reactive power measured data sequence can be expressed as: ; In the formula, Indicates the total amount of measured data extracted.

[0103] Step S05: constructing a single-machine infinite system of a wind turbine generator set;

[0104] To construct the simulation data sequence, we first need to build the single machine infinite bus system (SMIB) of the wind turbine. In power-related simulations, the single machine infinite bus system of the wind turbine is a simplified model in power system analysis. It is used to study the dynamic interaction between the generator and the power system.

[0105] Step S06: For each verification condition, the initial operating conditions of the wind turbine are set, and fault simulation is performed in combination with the basic control parameters and the voltage drop depth in the verification condition to obtain a voltage simulation sequence, an active power simulation sequence, and a reactive power simulation sequence as simulation data sequences for low-thru characteristic modeling of the wind turbine.

[0106] Select any verification condition and set the initial operating conditions of the wind turbine according to the verification condition. This includes terminal voltage, active power, and reactive power. Then set the simulation calculation step size and simulation calculation duration according to the basic control parameters. Then set the simulation fault according to the voltage drop depth in the verification condition and perform fault simulation.

[0107] According to this method, N verification conditions of the wind turbine are simulated one by one, and a voltage simulation data sequence, an active power simulation data sequence and a reactive power simulation data sequence for modeling the low-through characteristics of the wind turbine can be obtained.

[0108] Among them, in Under the following verification conditions:

[0109] The voltage simulation data sequence can be expressed as: ;

[0110] The active power simulation data sequence is expressed as: ;

[0111] The reactive power simulation data sequence is expressed as: .

[0112] Step 102, calculating the total mean absolute error under different verification conditions according to the measured data sequence and the simulation data sequence;

[0113] In this step, the main purpose is to calculate the mean absolute error based on the measured data sequence and the simulated data sequence.

[0114] In combination with the above content, it can be known that in the embodiment of the present invention, the measured data sequence mainly includes a voltage measured sequence , Active power measurement sequence And reactive power measurement sequence The simulation data sequence can mainly include the voltage simulation sequence , Active power simulation sequence and reactive power simulation sequence .

[0115] In a specific implementation, the process of calculating the total mean absolute error under different verification conditions according to the measured data sequence and the simulation data sequence may include the following sub-steps S11 to S16:

[0116] Step S11: identifying the starting measured data and the measured reference point of the voltage value suddenly dropping when the fault starts in the voltage measurement sequence, and identifying the starting simulation data and the simulation reference point of the voltage value suddenly dropping when the fault starts in the voltage simulation sequence;

[0117] First, in the voltage measured data sequence Identify the starting point data of the sudden drop in voltage value when the fault begins Specifically, identify The method is as follows:

[0118] First calculate the short-term mutation mean: .

[0119] in, is a given value, generally taken .

[0120] Then calculate the long-term mutation value: .

[0121] in, is a given value, generally taken .

[0122] The following criteria are used to determine the starting point:

[0123] like and , then judge It is the starting point where the voltage value suddenly drops downward.

[0124] In the formula, and are given thresholds. is the voltage measured data sequence Minimum possible fall depth .Pick is the voltage measured data sequence Minimum possible fall depth .

[0125] Similarly, the voltage measured data sequence can be identified The starting point where the voltage value suddenly recovers upward after the fault ends. Corresponding position Set as the measured reference point for the voltage measured data sequence, active power measured data sequence and reactive power measured data sequence.

[0126] Identify voltage simulation data sequence Initial simulation data of the voltage value suddenly dropping when the fault starts The method is similar to the previous identification of the starting measured data. The method is the same as that of , and will not be repeated here.

[0127] Step S12: According to the measured reference point and the simulated reference point, the measured active power sequence and the simulated active power sequence are aligned to obtain an active alignment sequence, and the measured reactive power sequence and the simulated reactive power sequence are aligned to obtain a reactive alignment sequence;

[0128] Step S12 mainly realizes the alignment of the measured data series and the simulation data series.

[0129] The active power measured sequence Active power simulation data series The data in is aligned according to the reference point position to obtain the active alignment sequence. The active alignment sequence is represented by the following array:

[0130]

[0131] in, Represents the voltage measured data sequence The corresponding active power measured sequence Measured reference point position Corresponding data; Represents voltage simulation data sequence The corresponding active power simulation data sequence Simulation reference point position Corresponding data; Indicates the number of data aligned on the right side of the reference point (here refers to both the measured reference point and the simulated reference point); Indicates the number of data aligned on the left side of the reference point (here refers to both the measured reference point and the simulated reference point).

[0132] Similarly, the reactive power measured data sequence can be obtained Reactive power simulation data series The reactive alignment sequence after alignment according to the reference point position is represented by the following array:

[0133]

[0134] in, Represents the voltage measured data sequence Corresponding reactive power measured data sequence Measured reference point position Corresponding data; Represents voltage simulation data sequence Corresponding reactive power simulation data sequence Simulation reference point position The corresponding data.

[0135] It should be noted that the number of data to the right and to the left of the reference point and are different. When aligning the measured sequence and the simulated sequence based on the reference point, the number of data on the left and right sides is determined by the number of data in the shorter sequence when aligning on the same side. Taking the reference point alignment to the left as an example, the number of data on the left side of the reference point of the final aligned sequence is determined according to the shorter sequence number of the sequence to the left of the reference point. For example, suppose there are 20 data points to the left of the measured reference point of the measured data sequence, and there are 25 data points to the left of the simulated reference point of the simulated data sequence to be aligned with it. Take the number of data points on the left side of the shorter measured data series (i.e. 20).

[0136] That is, when the position of the active power measured sequence and the active power simulation sequence are aligned based on the measured reference point and the simulation reference point, or the position of the reactive power measured sequence and the reactive power simulation sequence are aligned:

[0137] It is assumed that the measured data sequence has a first number of data on the left side of the measured reference point and a second number of data on the right side, and it is also assumed that the simulated data sequence has a third number of data on the left side of the simulated reference point and a fourth number of data on the right side.

[0138] For the number of data on the left side of the reference point of the sequence obtained after position alignment, if the first number is greater than the third number, the third number is used as the number of data on the left side of the reference point of the sequence obtained after position alignment. For example, if the measured data sequence has 20 data on the left side of the measured reference point and the simulation data sequence has 15 data on the left side of the simulation reference point, 15 is used as the number of data on the left side of the reference point of the sequence obtained after position alignment.

[0139] On the contrary, if the first number is less than the third number, the first number is used as the number of data located on the left side of the reference point in the sequence obtained after position alignment. For example, if the measured data sequence has 20 data on the left side of the measured reference point and the simulated data sequence has 30 data on the left side of the simulated reference point, 20 is used as the number of data located on the left side of the reference point in the sequence obtained after position alignment.

[0140] Similarly, for the number of data on the right side of the reference point of the sequence obtained after position alignment, if the second number is greater than the fourth number, the fourth number is used as the number of data on the right side of the reference point of the sequence obtained after position alignment. If the second number is less than the fourth number, the second number is used as the number of data on the right side of the reference point of the sequence obtained after position alignment.

[0141] Step S13: for each verification working condition, the active mean absolute error is calculated according to the active alignment sequence, and the reactive mean absolute error is calculated according to the reactive alignment sequence;

[0142] The active mean absolute error between the measured active power data and the simulated active power data is calculated by the following formula: :

[0143]

[0144] The reactive mean absolute error between the measured reactive power data and the simulated reactive power data is calculated by the following formula :

[0145]

[0146] Step S14: Calculate the total average absolute error of active power under different verification conditions according to each average absolute error of active power;

[0147] Calculated by the following formula The total mean absolute error of active power between measured data and simulated data under the verification conditions :

[0148]

[0149] Step S15: Calculate the total reactive power average absolute error under different verification conditions according to each reactive power average absolute error;

[0150] Calculated by the following formula The total mean absolute error of reactive power measured data and simulated data under the verification conditions :

[0151]

[0152] Step S16: taking the total average absolute error of active power and the total average absolute error of reactive power as the total average absolute error of the wind turbine generator set under different verification conditions.

[0153] From an overall perspective, the total mean absolute error of active power obtained in the previous steps can be And the total mean absolute error of reactive power , as a wind turbine in The total mean absolute error under different verification conditions.

[0154] Step 103, constructing a low penetration characteristic model of the wind turbine generator set; the low penetration characteristic model corresponds to the low penetration characteristic parameters to be solved;

[0155] Steps 103 to 104 mainly implement low penetration characteristic modeling and parameter fitting based on the particle swarm algorithm.

[0156] First, the active power characteristic model and reactive power characteristic model of the wind turbine generator system need to be set. For example, the active power characteristic after a wind farm fault occurs is shown in Figure 2 (a). The reactive power characteristic after a wind farm fault occurs is shown in Figure 2 (b).

[0157] The following formula is used to describe the post-fault low-power active characteristic model:

[0158]

[0159] in, express The voltage amplitude of the wind turbine terminal at the moment; Indicates the active current of the wind turbine before the fault; represents the rated capacity of the wind turbine; the active characteristic parameter to be determined is the active voltage coefficient , Active current coefficient , Active current setting value , Active power recovery coefficient and active power recovery value .

[0160] The following formula is used to describe the post-fault low-power reactive characteristic model:

[0161]

[0162] in, Indicates the given value of the wind turbine entering low penetration, generally 0.9pu; Represents the reactive current of the wind turbine before the fault; the reactive characteristic parameter to be determined is the reactive voltage coefficient , reactive current coefficient , reactive current setting value , reactive power recovery coefficient and reactive power recovery value .

[0163] Step 104, taking the minimum total mean absolute error as the objective function, performing particle swarm optimization on the low penetration characteristic parameters to obtain the adjusted low penetration characteristic parameters.

[0164] In combination with the foregoing content, it can be known that the low-through characteristic model in the embodiment of the present invention may include a low-through active characteristic model and a low-through reactive characteristic model. The low-through characteristic parameters to be solved may include active characteristic parameters of the low-through active characteristic model and reactive characteristic parameters of the low-through reactive characteristic model. In a specific implementation, the particle swarm optimization is performed on the low-through characteristic parameters with the minimum total mean absolute error as the objective function to obtain the low-through characteristic parameters after adjustment, which may include the following sub-steps S21 to S22:

[0165] Step S21: taking the minimum total average absolute error of active power as the first objective function and the active power characteristic parameter as the first optimization solution item, the particle swarm optimization algorithm is used to optimize and solve the active power characteristic parameter to obtain the set active power characteristic parameter;

[0166] Step S21 mainly implements solving active characteristic parameters by using particle swarm algorithm.

[0167] According to the previous description, it can be known that the active characteristic parameters include the active voltage coefficient , Active current coefficient , Active current setting value , Active power recovery coefficient And active power recovery value Specifically, the process of optimizing and solving the active characteristic parameters by using the particle swarm optimization algorithm to obtain the set active characteristic parameters can be achieved by executing the following sub-steps S211 to S216:

[0168] Step S211: taking the minimum total active average absolute error as the first fitness function;

[0169] First, set the fitness function of the particle swarm. Minimum (i.e. ) is the objective function and serves as the fitness function of the particle swarm, that is .

[0170] Step S212: using the active voltage coefficient, the active current coefficient, the active current setting value, the active recovery coefficient and the active recovery value as the position information of the particle and encoding them;

[0171] Set the particle position code. Use the active characteristic parameters to be determined in the low-throughput active characteristic model , , , and As the position information of the particle and encode it, that is:

[0172]

[0173] in, Indicates The position of a particle; express In the The value in particles; express In the The value in particles; express In the The value in particles; express In the The value in particles; express In the The value in the particles.

[0174] Step S213: using the active voltage coefficient change, the active current coefficient change, the active current setting value change, the active recovery coefficient change and the active recovery value change as the particle speed and encoding;

[0175] Set the particle velocity code. Use the active characteristic parameters to be determined in the low-throughput active characteristic model , , , and The change in is taken as the particle's velocity and encoded, namely:

[0176]

[0177] in, Indicates The speed of a particle; express In the The amount of change in each particle; express In the The amount of change in each particle; express In the The amount of change in each particle; express In the The amount of change in each particle; express In the The amount of change in a particle.

[0178] Step S214: obtaining an initial solution of an active voltage coefficient, an initial solution of an active current coefficient, an initial solution of an active current setting value, an initial solution of an active recovery coefficient, and an initial solution of an active recovery value by a heuristic method as initial particles of the first particle group;

[0179] When performing particle swarm optimization, the first generation of particles (the first generation of particles in the particle swarm) needs to be generated first.

[0180] Set the number of particles in the particle swarm. According to the problem to be solved, in the embodiment of the present invention, the number of particles is set to 50.

[0181] Particle swarm optimization is a heuristic optimization algorithm based on swarm intelligence. It seeks the global optimal solution by simulating the information exchange and cooperation between individuals in a swarm. Inspired by the foraging behavior of bird flocks, its initial process can also be called a heuristic method.

[0182] In order to speed up the particle swarm optimization, a heuristic method is used to obtain a set of initial solutions for active characteristic parameters. , , , and , as a first generation particle of the particle swarm. The process involves the following formula:

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190]

[0191] in, Indicates the active power measured sequence The average value of the active current from the beginning of the fault to the end of the fault; Indicates the active power measured sequence The corresponding average value of active current before the fault starts; Represents the voltage measured data sequence The average value of the corresponding voltage from the beginning of the fault to the end of the fault; Represents the voltage measured data sequence The mean value of the voltage before the fault starts; Indicates the active power measured sequence The starting point where the voltage value suddenly drops when the fault begins To the right individual data; Represents the voltage measured data sequence The starting point where the voltage value suddenly drops when the fault begins To the right individual data; Indicates that from the data The number of data from the starting point where the voltage value suddenly recovers upward after the fault ends; Indicates the active power measured sequence The individual data; Represents the voltage measured data sequence The data.

[0192] Step S215: performing iterative optimization and solving on the first generation particles of the first particle swarm in combination with the first fitness function until the maximum number of iterations is reached, and outputting the first optimal particle with the smallest fitness function value in the last generation particle swarm, the first optimal particle corresponding to the optimal active voltage coefficient, the optimal active current coefficient, the optimal active current setting value, the optimal active recovery coefficient and the optimal active recovery value;

[0193] Randomly set the first generation of particles The initial value of the particle The corresponding fitness function .

[0194] Will The particle corresponding to the minimum value in is taken as the initial optimal solution of the particle swarm, denoted as .

[0195] Then the next generation of particles is generated through iterative optimization.

[0196] Set up The particle swarm of the generation is . The particle with the smallest fitness function value in the particle swarm of the generation is recorded as:

[0197]

[0198] but The generation method of the particle swarm is as follows:

[0199]

[0200] in,

[0201]

[0202]

[0203]

[0204]

[0205]

[0206] Weight Parameters ,

[0207] Calculate the The fitness function of each particle in the particle swarm of the next generation. The particle with the smallest fitness function value is taken as the particle swarm. The optimal solution is:

[0208]

[0209] Refer to the above method to iterate and calculate the next generation of particle swarm until the maximum generation is reached. , generating the last generation of particles. Depending on the problem to be solved, the maximum number of generations of the particle swarm can be set .

[0210] The first The particle with the smallest fitness function value in the particle swarm of the generation Denoted as:

[0211]

[0212] Step S216: taking the optimal active voltage coefficient, the optimal active current coefficient, the optimal active current setting value, the optimal active recovery coefficient and the optimal active recovery value as the adjusted active characteristic parameters of the low-throughput active characteristic model.

[0213] The optimal active voltage coefficient , optimal active current coefficient , optimal active current setting value , optimal active power recovery coefficient And the optimal active power recovery value As the adjusted active characteristic parameter of the low-power active characteristic model.

[0214] Step S22: Taking the minimum total average absolute error of reactive power as the second objective function and the reactive characteristic parameter as the second optimization solution item, the reactive characteristic parameter is optimized and solved by using a particle swarm optimization algorithm to obtain the reactive characteristic parameter after setting.

[0215] According to the previous description, it can be known that the reactive characteristic parameters include the reactive voltage coefficient , reactive current coefficient , reactive current setting value , reactive power recovery coefficient And reactive power recovery value Referring to the above-mentioned optimization solution method for low-breakdown active characteristic parameters, the process of optimizing and solving reactive characteristic parameters by using particle swarm optimization algorithm to obtain the set reactive characteristic parameters may include the following sub-steps S221 to S226:

[0216] Step S221: taking the minimum total reactive power average absolute error as the second fitness function;

[0217] Step S222: using reactive voltage coefficient, reactive current coefficient, reactive current setting value, reactive recovery coefficient and reactive recovery value as particle position information and encoding;

[0218] Step S223: using the reactive voltage coefficient change, reactive current coefficient change, reactive current setting value change, reactive recovery coefficient change and reactive recovery value change as particle speeds and encoding;

[0219] Step S224: obtaining an initial solution of reactive voltage coefficient, an initial solution of reactive current coefficient, an initial solution of reactive current setting value, an initial solution of reactive recovery coefficient and an initial solution of reactive recovery value by means of a heuristic method as first generation particles of the second particle swarm;

[0220] Step S225: performing iterative optimization and solving on the first generation particles of the second particle swarm in combination with the second fitness function until the maximum number of iterations is reached, and outputting the second optimal particle with the smallest fitness function value in the last generation particle swarm, the second optimal particle corresponding to the optimal reactive voltage coefficient, the optimal reactive current coefficient, the optimal reactive current setting value, the optimal reactive recovery coefficient and the optimal reactive recovery value;

[0221] Step S226: taking the optimal reactive voltage coefficient, the optimal reactive current coefficient, the optimal reactive current setting value, the optimal reactive recovery coefficient and the optimal reactive recovery value as the adjusted reactive characteristic parameters of the low-through reactive characteristic model.

[0222] The specific execution process of each step in steps S221 to S226 can refer to the corresponding steps in the aforementioned steps S211 to S216, and will not be repeated here.

[0223] In an embodiment of the present invention, the total mean absolute error under different verification conditions is calculated in combination with actual measurement and simulation, and the minimum total mean absolute error is used as the objective function. The low-through characteristic parameters to be solved in the low-through characteristic model of the wind turbine are further optimized and solved based on the particle swarm optimization algorithm. This can handle the problem of setting the low-through characteristic parameters in the wind farm equivalent process and meet the wind farm equivalent modeling requirements.

[0224] For better explanation, refer to Figure 3 , showing a schematic diagram of the overall process of a method for setting low-through characteristic parameters applicable to wind farms and the like provided by an embodiment of the present invention. It should be pointed out that this embodiment only briefly describes the general process of setting low-through characteristic parameters applicable to wind farms and the like, 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 in detail. It can be understood that the present invention is not limited to this.

[0225] Step 301: Determine the basic control parameters required for low penetration characteristic modeling and parameter setting of the wind turbine generator set;

[0226] Step 302: Based on the basic control parameters, a measured data sequence is constructed in combination with the verification working condition and the measured data, and a simulation data sequence is constructed in combination with the verification working condition and the fault simulation;

[0227] Step 303: Calculate the total average absolute error of active power and the total average absolute error of reactive power under different verification conditions according to the measured data sequence and the simulation data sequence;

[0228] Step 304: constructing a low-power active characteristic model and a low-power reactive characteristic model of the wind turbine generator set; wherein the low-power active characteristic model corresponds to the active characteristic parameters to be solved, and the low-power reactive characteristic model corresponds to the reactive characteristic parameters to be solved;

[0229] Step 305: Taking the minimum total average absolute error of active power as the first objective function and the active power characteristic parameter as the first optimization solution item, the particle swarm optimization algorithm is used to optimize and solve the active power characteristic parameter to obtain the set active power characteristic parameter;

[0230] Step 306: Taking the minimum total mean absolute error of reactive power as the second objective function and the reactive characteristic parameter as the second optimization solution item, the reactive characteristic parameter is optimized and solved by using a particle swarm optimization algorithm to obtain the set reactive characteristic parameter.

[0231] Reference Figure 4 , shows a structural block diagram of a low-throughput characteristic parameter setting device applicable to a wind farm equivalent provided by an embodiment of the present invention, which may specifically include:

[0232] A data sequence construction module 401 is used to construct a measured data sequence and a simulation data sequence for low-throughput characteristic modeling of a wind turbine generator set;

[0233] A total mean absolute error calculation module 402 is used to calculate the total mean absolute error under different verification conditions according to the measured data sequence and the simulation data sequence;

[0234] A low penetration characteristic model building module 403 is used to build a low penetration characteristic model of the wind turbine generator set; the low penetration characteristic model corresponds to the low penetration characteristic parameters to be solved;

[0235] The parameter setting module 404 is used to perform particle swarm optimization on the low penetration characteristic parameters by taking the minimum total mean absolute error as the objective function to obtain the low penetration characteristic parameters after setting.

[0236] In an optional embodiment, the measured data sequence includes a voltage measured sequence, an active power measured sequence, and a reactive power measured sequence; the simulation data sequence includes a voltage simulation sequence, an active power simulation sequence, and a reactive power simulation sequence; the total mean absolute error calculation module 402 includes:

[0237] A reference point identification module, used to identify the starting measured data and the measured reference point of the voltage value suddenly falling when a fault starts in the voltage measurement sequence, and to identify the starting simulation data and the simulation reference point of the voltage value suddenly falling when a fault starts in the voltage simulation sequence;

[0238] A position alignment module, used to align the active power measured sequence with the active power simulation sequence according to the measured reference point and the simulation reference point to obtain an active power alignment sequence, and simultaneously align the reactive power measured sequence with the reactive power simulation sequence to obtain a reactive power alignment sequence;

[0239] A mean absolute error calculation module, used for calculating the active mean absolute error according to the active alignment sequence and calculating the reactive mean absolute error according to the reactive alignment sequence for each verification working condition;

[0240] An active total average absolute error calculation module is used to calculate the active total average absolute error under different verification conditions according to each of the active average absolute errors;

[0241] A reactive total average absolute error calculation module, used to calculate the reactive total average absolute error under different verification conditions according to each reactive average absolute error;

[0242] The total average absolute error determination module is used to use the total average absolute error of active power and the total average absolute error of reactive power as the total average absolute error of the wind turbine generator set under different verification conditions.

[0243] In an optional embodiment, the position alignment module is specifically used to:

[0244] When the active power measured sequence and the active power simulation sequence are aligned based on the measured reference point and the simulation reference point, or when the reactive power measured sequence and the reactive power simulation sequence are aligned, it is assumed that the measured data sequence has a first number of data on the left side of the measured reference point and a second number of data on the right side; the simulation data sequence has a third number of data on the left side of the simulation reference point and a fourth number of data on the right side;

[0245] If the first number is greater than the third number, the third number is used as the number of data located on the left side of the reference point in the sequence obtained after position alignment;

[0246] If the first number is smaller than the third number, the first number is used as the number of data located on the left side of the reference point in the sequence obtained after position alignment;

[0247] If the second number is greater than the fourth number, the fourth number is used as the number of data located on the right side of the reference point in the sequence obtained after position alignment;

[0248] If the second number is smaller than the fourth number, the second number is used as the number of data located on the right side of the reference point in the sequence obtained after the position alignment.

[0249] In an optional embodiment, the low-throughput characteristic model includes a low-throughput active characteristic model and a low-throughput reactive characteristic model; the low-throughput characteristic parameters to be solved include active characteristic parameters of the low-throughput active characteristic model and reactive characteristic parameters of the low-throughput reactive characteristic model; the parameter setting module 404 includes:

[0250] An active characteristic parameter setting module is used to use the minimum total active mean absolute error as the first objective function, the active characteristic parameter as the first optimization solution item, and use a particle swarm optimization algorithm to optimize and solve the active characteristic parameter to obtain the set active characteristic parameter;

[0251] The reactive characteristic parameter setting module is used to take the minimum total reactive average absolute error as the second objective function, take the reactive characteristic parameter as the second optimization solution item, adopt the particle swarm optimization algorithm to optimize the reactive characteristic parameter, and obtain the set reactive characteristic parameter.

[0252] In an optional embodiment, the active characteristic parameters include an active voltage coefficient, an active current coefficient, an active current setting value, an active recovery coefficient, and an active recovery value, and the active characteristic parameter setting module includes:

[0253] A first fitness function determination module, configured to take the minimum total active mean absolute error as the first fitness function;

[0254] A first position information encoding module, used for encoding the active voltage coefficient, the active current coefficient, the active current setting value, the active recovery coefficient and the active recovery value as the position information of the particle;

[0255] A first speed encoding module is used to encode the active voltage coefficient change, the active current coefficient change, the active current setting value change, the active recovery coefficient change and the active recovery value change as the particle speed;

[0256] A first particle swarm initial generation particle determination module, used to obtain an initial solution of an active voltage coefficient, an initial solution of an active current coefficient, an initial solution of an active current setting value, an initial solution of an active recovery coefficient, and an initial solution of an active recovery value as initial generation particles of the first particle swarm by a heuristic method;

[0257] A first optimal particle iterative solution module, used for iteratively optimizing and solving the first generation particles of the first particle swarm in combination with the first fitness function until the maximum number of iterations is reached, and outputting the first optimal particle with the smallest fitness function value in the last generation particle swarm, wherein the first optimal particle corresponds to an optimal active voltage coefficient, an optimal active current coefficient, an optimal active current setting value, an optimal active recovery coefficient, and an optimal active recovery value;

[0258] The module for determining the active power characteristic parameters after adjustment is used to use the optimal active voltage coefficient, the optimal active current coefficient, the optimal active current setting value, the optimal active recovery coefficient and the optimal active recovery value as the active power characteristic parameters after adjustment of the low-breakdown active power characteristic model.

[0259] In an optional embodiment, the reactive characteristic parameters include a reactive voltage coefficient, a reactive current coefficient, a reactive current setting value, a reactive recovery coefficient, and a reactive recovery value, and the reactive characteristic parameter setting module includes:

[0260] A second fitness function determination module, configured to use the minimum total reactive power average absolute error as the second fitness function;

[0261] A second position information encoding module, used to encode the reactive voltage coefficient, the reactive current coefficient, the reactive current setting value, the reactive recovery coefficient and the reactive recovery value as the position information of the particle;

[0262] A second speed encoding module is used to encode the reactive voltage coefficient change, reactive current coefficient change, reactive current setting value change, reactive recovery coefficient change and reactive recovery value change as the speed of the particle;

[0263] A first-generation particle determination module of the second particle swarm is used to obtain an initial solution of a reactive voltage coefficient, an initial solution of a reactive current coefficient, an initial solution of a reactive current setting value, an initial solution of a reactive recovery coefficient, and an initial solution of a reactive recovery value by a heuristic method as the first-generation particles of the second particle swarm;

[0264] A second optimal particle iterative solution module is used to iteratively optimize and solve the first-generation particles of the second particle swarm in combination with the second fitness function until the maximum number of iterations is reached, and output the second optimal particle with the smallest fitness function value in the last generation of particle swarm, wherein the second optimal particle corresponds to an optimal reactive voltage coefficient, an optimal reactive current coefficient, an optimal reactive current setting value, an optimal reactive recovery coefficient, and an optimal reactive recovery value;

[0265] The module for determining reactive characteristic parameters after adjustment is used to use the optimal reactive voltage coefficient, the optimal reactive current coefficient, the optimal reactive current setting value, the optimal reactive recovery coefficient and the optimal reactive recovery value as the reactive characteristic parameters after adjustment of the low-throughput reactive characteristic model.

[0266] In an optional embodiment, the wind turbine generator system corresponds to a plurality of verification conditions, and each verification condition corresponds to a set of measured data consisting of three-phase instantaneous measurement values ​​of voltage and three-phase instantaneous measurement values ​​of current; the data sequence construction module 401 includes:

[0267] A basic control parameter determination module is used to determine the basic control parameters required for the wind turbine generator set in low-through characteristic modeling and parameter setting; the basic control parameters include a capacity base value, a voltage reference value, a fundamental wave period value, a simulation calculation step size, and a simulation calculation duration;

[0268] A fundamental component calculation module, for calculating, for each of the verification conditions, the voltage fundamental positive sequence component, the active power fundamental positive sequence component and the reactive power fundamental positive sequence component under the verification condition according to the basic control parameters, the voltage three-phase instantaneous measurement values ​​and the current three-phase instantaneous measurement values;

[0269] An extraction interval calculation module, used to calculate the extraction interval and the total amount of the measured data by combining the sampling interval of the measured data, the simulation calculation step length and the simulation calculation time length;

[0270] An interval sampling module, for sampling the voltage fundamental positive sequence component, the active power fundamental positive sequence component and the reactive power fundamental positive sequence component at intervals according to the sampling interval and the total amount, respectively, to obtain a voltage measured sequence, an active power measured sequence and a reactive power measured sequence as a measured data sequence for low-throughput characteristic modeling of the wind turbine generator set;

[0271] A single-machine infinite system building module, used to build a single-machine infinite system of the wind turbine generator set;

[0272] A fault simulation module is used to set the initial operating conditions of the wind turbine for each of the verification conditions, and to perform fault simulation in combination with the basic control parameters and the voltage drop depth in the verification conditions, to obtain a voltage simulation sequence, an active power simulation sequence, and a reactive power simulation sequence as simulation data sequences for low-thru characteristic modeling of the wind turbine.

[0273] 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.

[0274] 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 terms such as first and second to distinguish and explain some technical features. The terms such as first and second 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.

[0275] An embodiment of the present invention further provides an electronic device, the device comprising a processor and a memory:

[0276] The memory is used to store the program code and transmit the program code to the processor;

[0277] The processor is used to execute the low-throughput characteristic parameter setting method applicable to wind farm equivalent values ​​according to any embodiment of the present invention according to the instructions in the program code.

[0278] 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 low-throughput characteristic parameter setting method applicable to wind farm equivalent values ​​according to any embodiment of the present invention.

[0279] 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.

[0280] 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.

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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 setting low penetration characteristic parameters applicable to wind farms, characterized in that: include: Construct measured data sequences and simulation data sequences for wind turbines to model low penetration characteristics; Calculating the total mean absolute error under different verification conditions according to the measured data sequence and the simulation data sequence; Constructing a low penetration characteristic model of the wind turbine generator set; the low penetration characteristic model corresponds to the low penetration characteristic parameters to be solved; Taking the minimum total mean absolute error as the objective function, the low penetration characteristic parameters are solved by particle swarm optimization to obtain the adjusted low penetration characteristic parameters.

2. The method for setting the low penetration characteristic parameters according to claim 1, characterized in that: The measured data sequence includes a voltage measured sequence, an active power measured sequence and a reactive power measured sequence; the simulation data sequence includes a voltage simulation sequence, an active power simulation sequence and a reactive power simulation sequence; The calculating the total mean absolute error under different verification conditions according to the measured data sequence and the simulation data sequence includes: Identify the starting measured data and the measured reference point of the voltage value suddenly dropping downward when the fault starts in the voltage measured sequence, and identify the starting simulation data and the simulation reference point of the voltage value suddenly dropping downward when the fault starts in the voltage simulation sequence; According to the measured reference point and the simulated reference point, the measured active power sequence and the simulated active power sequence are aligned to obtain an active power alignment sequence, and the measured reactive power sequence and the simulated reactive power sequence are aligned to obtain a reactive power alignment sequence; For each verification working condition, the active mean absolute error is calculated according to the active alignment sequence, and the reactive mean absolute error is calculated according to the reactive alignment sequence; Calculate the total average absolute error of active power under different verification conditions according to each of the average absolute errors of active power; Calculate the total reactive power average absolute error under different verification conditions according to each reactive power average absolute error; The total average absolute error of active power and the total average absolute error of reactive power are taken as the total average absolute error of the wind turbine generator set under different verification conditions.

3. The method for setting the low penetration characteristic parameters according to claim 2 is characterized in that: Also includes: When the active power measured sequence and the active power simulation sequence are aligned based on the measured reference point and the simulation reference point, or when the reactive power measured sequence and the reactive power simulation sequence are aligned, it is assumed that the measured data sequence has a first number of data on the left side of the measured reference point and a second number of data on the right side; the simulation data sequence has a third number of data on the left side of the simulation reference point and a fourth number of data on the right side; If the first number is greater than the third number, the third number is used as the number of data located on the left side of the reference point in the sequence obtained after position alignment; If the first number is smaller than the third number, the first number is used as the number of data located on the left side of the reference point in the sequence obtained after position alignment; If the second number is greater than the fourth number, the fourth number is used as the number of data located on the right side of the reference point in the sequence obtained after position alignment; If the second number is smaller than the fourth number, the second number is used as the number of data located on the right side of the reference point in the sequence obtained after the position alignment.

4. The method for setting the low penetration characteristic parameters according to claim 2 is characterized in that: The low-through characteristic model includes a low-through active characteristic model and a low-through reactive characteristic model; the low-through characteristic parameters to be solved include active characteristic parameters of the low-through active characteristic model and reactive characteristic parameters of the low-through reactive characteristic model; the method of performing particle swarm optimization on the low-through characteristic parameters with the minimum total mean absolute error as the objective function to obtain the set low-through characteristic parameters includes: Taking the minimum total active mean absolute error as the first objective function and the active characteristic parameter as the first optimization solution item, a particle swarm optimization algorithm is used to optimize and solve the active characteristic parameter to obtain the set active characteristic parameter; The minimum total reactive power average absolute error is taken as the second objective function, the reactive characteristic parameter is taken as the second optimization solution item, and the reactive characteristic parameter is optimized and solved by using a particle swarm optimization algorithm to obtain the set reactive characteristic parameter.

5. The method for setting the low penetration characteristic parameters according to claim 4 is characterized in that: The active characteristic parameters include an active voltage coefficient, an active current coefficient, an active current setting value, an active recovery coefficient, and an active recovery value. The particle swarm optimization algorithm is used to optimize and solve the active characteristic parameters to obtain the set active characteristic parameters, including: Taking the minimum total active average absolute error as the first fitness function; The active voltage coefficient, the active current coefficient, the active current setting value, the active recovery coefficient and the active recovery value are used as the position information of the particle and encoded; The active voltage coefficient change, the active current coefficient change, the active current setting value change, the active recovery coefficient change and the active recovery value change are used as the particle speed and encoded; The initial solution of active voltage coefficient, the initial solution of active current coefficient, the initial solution of active current setting value, the initial solution of active recovery coefficient and the initial solution of active recovery value are obtained by heuristic method as the initial generation particles of the first particle group; Iteratively optimizing and solving the first generation particles of the first particle swarm in combination with the first fitness function until a maximum number of iterations is reached, and outputting a first optimal particle with the smallest fitness function value in the last generation particle swarm, wherein the first optimal particle corresponds to an optimal active voltage coefficient, an optimal active current coefficient, an optimal active current setting value, an optimal active recovery coefficient, and an optimal active recovery value; The optimal active voltage coefficient, the optimal active current coefficient, the optimal active current setting value, the optimal active recovery coefficient and the optimal active recovery value are used as the adjusted active characteristic parameters of the low-power active characteristic model.

6. The method for setting the low penetration characteristic parameters according to claim 4, characterized in that: The reactive characteristic parameters include reactive voltage coefficient, reactive current coefficient, reactive current setting value, reactive recovery coefficient and reactive recovery value. The particle swarm optimization algorithm is used to optimize and solve the reactive characteristic parameters to obtain the set reactive characteristic parameters, including: Taking the minimum total reactive power average absolute error as the second fitness function; The reactive voltage coefficient, the reactive current coefficient, the reactive current setting value, the reactive recovery coefficient and the reactive recovery value are used as the position information of the particle and encoded; The reactive voltage coefficient change, the reactive current coefficient change, the reactive current setting value change, the reactive recovery coefficient change and the reactive recovery value change are used as the particle speed and encoded; The initial solutions of reactive voltage coefficient, reactive current coefficient, reactive current setting value, reactive recovery coefficient and reactive recovery value are obtained by heuristic method as the first generation particles of the second particle swarm; In combination with the second fitness function, the first generation particles of the second particle swarm are iteratively optimized and solved until the maximum number of iterations is reached, and the second optimal particle with the smallest fitness function value in the last generation of particle swarm is output, and the second optimal particle corresponds to the optimal reactive voltage coefficient, the optimal reactive current coefficient, the optimal reactive current setting value, the optimal reactive recovery coefficient and the optimal reactive recovery value; The optimal reactive voltage coefficient, the optimal reactive current coefficient, the optimal reactive current setting value, the optimal reactive recovery coefficient and the optimal reactive recovery value are used as the adjusted reactive characteristic parameters of the low-throughput reactive characteristic model.

7. The method for setting the low penetration characteristic parameters according to any one of claims 1 to 6, characterized in that: The wind turbine generator system corresponds to a plurality of verification working conditions, and each verification working condition corresponds to a set of measured data consisting of three-phase instantaneous measurement values ​​of voltage and three-phase instantaneous measurement values ​​of current; The method of constructing a measured data sequence and a simulation data sequence for low penetration characteristic modeling of a wind turbine generator system includes: Determine the basic control parameters required for low-through characteristic modeling and parameter setting of the wind turbine generator set; the basic control parameters include a capacity base value, a voltage reference value, a fundamental wave period value, a simulation calculation step size, and a simulation calculation duration of simulation calculation; For each of the verification conditions, according to the basic control parameters, the three-phase instantaneous measurement values ​​of the voltage, and the three-phase instantaneous measurement values ​​of the current, calculate the voltage fundamental positive sequence component, the active power fundamental positive sequence component, and the reactive power fundamental positive sequence component under the verification condition; Calculate the sampling interval and the total amount of the measured data based on the sampling interval of the measured data, the simulation calculation step length and the simulation calculation time length; According to the sampling interval and the total amount of sampling, the voltage fundamental positive sequence component, the active power fundamental positive sequence component and the reactive power fundamental positive sequence component are sampled at intervals to obtain a voltage measured sequence, an active power measured sequence and a reactive power measured sequence as a measured data sequence for low-throughput characteristic modeling of the wind turbine generator set; Constructing a single-machine infinite system of the wind turbine generator set; For each of the verification conditions, the initial operating conditions of the wind turbine are set, and fault simulation is performed in combination with the basic control parameters and the voltage drop depth in the verification conditions to obtain a voltage simulation sequence, an active power simulation sequence, and a reactive power simulation sequence as simulation data sequences for low-thru characteristic modeling of the wind turbine.

8. A low penetration characteristic parameter setting device suitable for wind farms, characterized in that: include: A data sequence construction module is used to construct measured data sequences and simulation data sequences for low penetration characteristic modeling of wind turbines; A total mean absolute error calculation module, used to calculate the total mean absolute error under different verification conditions according to the measured data sequence and the simulation data sequence; A low penetration characteristic model building module is used to build a low penetration characteristic model of the wind turbine generator set; the low penetration characteristic model corresponds to the low penetration characteristic parameters to be solved; The parameter setting module is used to use the minimum total mean absolute error as the objective function, perform particle swarm optimization on the low penetration characteristic parameters, and obtain the low penetration characteristic parameters after setting.

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 low-throughput characteristic parameter setting method applicable to wind farm equivalent values ​​as described in any one of claims 1-7 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 low-throughput characteristic parameter setting method applicable to wind farm equivalents as described in any one of claims 1 to 7.

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