PSD-BPA model setting and verification method based on tree species optimization algorithm

Abstract

The invention provides a PSD-BPA model setting and verification method based on a tree species optimization algorithm, and relates to the technical field of power analysis, and the method comprises the following steps: S1, reading information collection data, and filling in a model card of a PSD-BPA model; s2, reading semi-physical simulation test data, performing parameter preliminary identification, generating SVG model parameters, and writing the SVG model parameters into a PSD-BPA model card; s3, constructing an initial tree group in a TSA search stage, selecting an initial parameter of each working condition as an initial point, and obtaining an initial solution; s4, compiling a fault voltage ride-through test power flow model corresponding to the SVG model based on the PSD-BPA; according to the method, electromechanical modeling is carried out on the basis of a PSD-BPA platform, parameter identification is carried out on multiple sets of parameters of various fault working conditions in combination with a tree species optimization algorithm TSA, the deviation specified by regulations serves as verification reference, and the result shows that the identification result is good in compatibility and can meet the deviation requirement.

Description

Technical Field

[0001] The present invention relates to the technical field of power analysis, and in particular to a PSD-BPA model setting and verification method based on a tree species optimization algorithm. Background Art

[0002] The specification for the grid-connected performance test of the dynamic reactive power compensation device of a wind farm specifies the test content, test conditions, test methods and evaluation of the test results of the grid-connected performance test of the dynamic reactive power compensation device of a wind farm. The dynamic reactive power compensation device shall meet the fault ride-through control strategy, with the reactive power at the connection point of the device as the control target. Before the fault, the device operates under four power output conditions: inductive low power output (20% rated power), inductive high power output (100% rated power), capacitive low power output (20% rated power), and capacitive high power output (100% rated power). By controlling the fault simulation device, the voltage at the connection point produces a three-phase symmetrical voltage drop / increase and a two-phase voltage drop / increase.

[0003] As the proportion of new energy electric fields in the power grid increases, the importance of reactive compensation equipment becomes more and more prominent. At present, the PSD-BPA (Power System Dynamic-Bonneville Power Administration) power system analysis program is one of the main simulation platforms for verifying the high and low voltage crossing of reactive compensation equipment. The parameter identification of the electromechanical simulation model is based on the high and low voltage crossing test data or semi-physical simulation test data on site. The high and low voltage crossing test on site costs a lot of manpower, material resources and financial resources, and the data obtained is more reliable. Semi-physical simulation tests can greatly save manpower, material resources and financial resources, and the data obtained is usually more reliable. Therefore, most of the current electromechanical simulation modeling adopts semi-physical simulation tests. However, the results obtained by parameter identification based on the data obtained from the semi-physical simulation test are often not fully compatible with all fault conditions. Therefore, the present invention proposes a PSD-BPA model setting and verification method based on a tree species optimization algorithm to solve the problems existing in the prior art. Summary of the invention

[0004] In view of the above problems, the present invention proposes a PSD-BPA model tuning and verification method based on a tree species optimization algorithm. The PSD-BPA model tuning and verification method based on a tree species optimization algorithm performs electromechanical modeling based on the PSD-BPA platform, combines the tree species optimization algorithm TSA, performs parameter identification on multiple groups of parameters for various fault conditions, and uses the deviations specified in the regulations as a verification reference. The results show that the identification results have good compatibility and can meet the deviation requirements.

[0005] To achieve the purpose of the present invention, the present invention is implemented by the following technical scheme: a PSD-BPA model tuning and verification method based on a tree species optimization algorithm comprises the following steps:

[0006] S1: Read the funding information and fill in the model card of the PSD-BPA model;

[0007] S2: Read the semi-physical simulation test data, perform preliminary parameter identification, generate SVG model parameters, and write them into the PSD-BPA model card;

[0008] S3: construct the initial tree group of the TSA search phase, select the initial parameters of each working condition as the initial point, and obtain the initial solution;

[0009] S4: Write a fault voltage ride-through test power flow model corresponding to the SVG model based on PSD-BPA, and verify the deviation between the initial voltage of PSD-BPA simulation and the initial voltage and initial reactive power of the semi-physical simulation test;

[0010] S5: Write a transient model for fault voltage ride-through test corresponding to the BPA-based SVG model, and verify the deviation between the PSD-BPA simulated fault steady-state voltage and the semi-physical simulated fault steady-state voltage;

[0011] S6: Based on S5, the reactive power and reactive current of the BPA simulation data and the semi-physical simulation data under each working condition are verified by referring to the regulations for deviation value;

[0012] S7: Classify all control parameters that meet the deviation value verification through TSA, obtain several sets of control parameters to complete identification verification, and output the control parameter results.

[0013] Further improvement is that in S1, the model cards of the PSD-BPA model include a control system model (VG), an additional control card (VG+), a dead zone card (VGQ-DBBD), a device constant reactive power card (VGQ-QCON), a constant voltage constant reactive power mode switching card (VGQ-QVSW), an SVG low voltage ride-through card (VGQ-LVRT), an SVG high voltage ride-through card (VGQ-HVRT), and a voltage protection card (RE).

[0014] Further improvement is that in S2, semi-physical simulation test data is read through MATLAB, reactive power and reactive current before voltage fault and during fault steady state are counted, current recovery rate during fault recovery is calculated, and consistency of different test data within the same group is verified. When the data is inconsistent, it indicates that the SVG control device cannot maintain the same response characteristics under the same test conditions, and the test cannot be repeated and reproduced.

[0015] A further improvement is that: S2 specifically includes the following steps:

[0016] S21: SVG controller model parameters, which identifies the voltage threshold for entering and exiting high / low voltage ride-through through the reactive power size under various working conditions;

[0017] S22: Voltage ride-through control parameters. By analyzing the data under various working conditions and taking overcurrent protection as the boundary condition, the relationship between reactive current and voltage during the fault voltage ride-through period is identified.

[0018] During fault ride-through, the fitting formula of reactive current command is as follows:

[0019] I t =I eq +K(V ref -V t )

[0020] Among them, I t Indicates the current during the low-breakthrough process; I eq Indicates the average current of the previous N steps at the end of the low-pass delay; K, coefficient; V ref , Installation point reference voltage, the default value is 0.9 for low voltage ride-through and 1.1 for high voltage ride-through; V t , installation point voltage;

[0021] S23: Generate SVG model parameters based on the identification results of the above physical parameters, operating parameters and initially set control parameters, and write them into the PSD-BPA model card.

[0022] A further improvement is that in S3, an initial tree group of the TSA search phase is constructed, and the initial voltage, reactive current, reactive power and voltage value of each operating condition are selected as initial points. Through the action of the TSA initial tree group, the voltage value of the large power grid arrangement, the voltage and reactive power of the SVG equipment and the solution of the ground fault impedance are obtained as the initial solution.

[0023] A further improvement is that in S4, when the deviation is verified, if the deviation is not met, the voltage value arranged by the large power grid and the voltage value arranged by the SVG are adjusted, and the fault voltage ride-through test flow model is run again to limit the BPA simulation voltage and the initial voltage and actual reactive power of the semi-physical simulation test within the deviation range.

[0024] A further improvement is that in S5, when the deviation is not satisfied during the deviation verification, local control parameter optimization is performed through TSA until the PSD-BPA simulated fault steady-state voltage and the semi-physical simulated fault steady-state voltage are limited within the deviation range.

[0025] A further improvement is that in S6, when the deviation value is verified, if it is not satisfied, optimization is performed through TSA until the reactive power and reactive current of the BPA simulation data and the semi-physical simulation data under each working condition are limited within the deviation value range.

[0026] A further improvement is that in S7, all control parameters that meet the deviation value verification are classified to obtain 1 set of control parameters that complete identification verification, 2 sets of control parameters that complete identification verification, and 4 sets of control parameters that complete identification verification, and output the control parameter results.

[0027] The beneficial effects of the present invention are:

[0028] 1. The present invention uses the PSD-BPA platform as the basis for electromechanical modeling, combines the tree species optimization algorithm TSA, performs parameter identification for multiple groups of parameters under various fault conditions, and uses the deviation specified in the regulations as a verification reference. The results show that the identification results have good compatibility and can meet the deviation requirements.

[0029] 2. The present invention is based on the TSA architecture and is compatible with all verification types, data sources, types of working conditions, and numbers of working conditions. It can complete the electromechanical parameter identification + model verification of SVG based on test data, and can also complete model verification or data verification based on test data + modeling report. It has a good processing capability for the identification of relevant parameters in SVG modeling built by PSD-BPA. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is the principle flow chart of the present invention;

[0031] Figure 2 It is the algorithm flow chart of the present invention. DETAILED DESCRIPTION

[0032] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0033] Embodiment 1

[0034] according to Figure 1 , 2 As shown, this embodiment proposes a PSD-BPA model tuning and verification method based on a tree species optimization algorithm, comprising the following steps:

[0035] S1: Read the collection data and fill in the model card of the PSD-BPA model; including the control system model (VG), additional control card (VG+), dead zone card (VGQ-DBBD), device constant reactive power card (VGQ-QCON), constant voltage constant reactive power mode switching card (VGQ-QVSW), SVG low voltage ride-through card (VGQ-LVRT), SVG high voltage ride-through card (VGQ-HVRT), and voltage protection card (RE);

[0036] S2: Read the semi-physical simulation test data, perform preliminary parameter identification, generate SVG model parameters, and write them into the PSD-BPA model card; read the semi-physical simulation test data through MATLAB, count the reactive power and reactive current before the voltage fault and during the fault steady state, calculate the current recovery rate during the fault recovery period, and verify the consistency of different test data in the same group. When the data is inconsistent, it indicates that the SVG control device cannot maintain the same response characteristics under the same test conditions, and the test cannot be repeated and reproduced; specifically, the following steps are included:

[0037] SVG controller model parameters, through the reactive power size under various working conditions, identify the voltage threshold value of entering and exiting high / low voltage ride-through;

[0038] Voltage ride-through control parameters, by analyzing the data under various working conditions, taking overcurrent protection as the boundary condition, the relationship between reactive current and voltage during fault voltage ride-through is identified.

[0039] During fault ride-through, the fitting formula of reactive current command is as follows:

[0040] I t =I eq +K(V ref -V t )

[0041] Among them, I t Indicates the current during the low-breakthrough process; I eq Indicates the average current of the previous N steps at the end of the low-pass delay; K, coefficient; V ref , Installation point reference voltage, the default value is 0.9 for low voltage ride-through and 1.1 for high voltage ride-through; V t , installation point voltage;

[0042] The above physical parameters, operating parameters and initial setting control parameter identification results are used to generate SVG model parameters and written into the PSD-BPA model card;

[0043] S3: construct the initial tree group of the TSA search phase, select the initial parameters of each working condition as the initial point, and obtain the initial solution; select the initial voltage, reactive current, reactive power and voltage value of each working condition as the initial point, and through the action of the TSA initial tree group, take the voltage value of the large power grid arrangement, the voltage and reactive power of the SVG equipment and the solution of the ground fault impedance as the initial solution;

[0044] S4: Write a fault voltage ride-through test flow model corresponding to the SVG model based on PSD-BPA, and verify the deviation between the PSD-BPA simulated initial voltage and the initial voltage and initial reactive power of the semi-physical simulation test; if the deviation is not satisfied, adjust the voltage value arranged by the large power grid and the voltage value arranged by SVG, and run the fault voltage ride-through test flow model again, and limit the BPA simulated voltage and the initial voltage and initial reactive power of the semi-physical simulation test within the deviation range;

[0045] S5: Write a fault voltage ride-through test transient model corresponding to the BPA-based SVG model, and verify the deviation between the PSD-BPA simulated fault steady-state voltage and the semi-physical simulated fault steady-state voltage; if the deviation is not met, perform local control parameter optimization through TSA until the PSD-BPA simulated fault steady-state voltage and the semi-physical simulated fault steady-state voltage are limited within the deviation range;

[0046] S6: Based on S5, the reactive power and reactive current of the BPA simulation data and the semi-physical simulation data under each working condition are verified with reference to the regulations for deviation values; if they are not satisfied, they are optimized through TSA until the reactive power and reactive current of the BPA simulation data and the semi-physical simulation data under each working condition are limited within the deviation value range;

[0047] S7: Classify all control parameters that meet the deviation value verification through TSA, obtain 1 set of control parameters that complete identification verification, 2 sets of control parameters that complete identification verification, and 4 sets of control parameters that complete identification verification, and output the control parameter results.

[0048] Embodiment 2

[0049] according to Figure 1 , 2 As shown, this embodiment proposes a PSD-BPA model tuning and verification method based on a tree species optimization algorithm.

[0050] The dynamic reactive power compensation device should meet the fault ride-through control strategy. Taking the reactive power of the device's connection point as the control target, the device operates under four power output conditions before the fault: inductive low power output (20% rated power), inductive high power output (100% rated power), capacitive low power output (20% rated power), and capacitive high power output (100% rated power). Through the control of the fault simulation device, the connection point voltage produces a three-phase symmetrical voltage drop / increase and a two-phase voltage drop / increase. The amplitude and duration of the drop / increase are shown in the table below.

[0051] Table 1 Voltage drop specifications

[0052]

[0053] Table 2 Voltage boost specifications

[0054]

[0055] The present invention uses the PSD-BPA platform as the basis for electromechanical modeling, combines the Tree Seed Algorithm (TSA), and performs parameter identification for multiple groups of parameters under various fault conditions. The deviation specified in NB / T31075-2016 "Modeling and Verification Procedure for Electrical Simulation Model of Wind Farm" is used as a verification reference (as shown in Table 3). The results show that the identification results have good compatibility and can meet the deviation requirements.

[0056] Table 3 Maximum allowable deviation of SVG fault ride-through characteristics electromechanical transient model verification

[0057]

[0058] The parameters of the reactive power compensation device simulation model are generally divided into physical parameters, operating parameters, and control parameters. Among them, physical parameters and operating parameters can be directly obtained from the collection table, and control parameters are the main identification parameters. Usually, the key parameters of SVG identification include parameters of the four main links: judgment of in / out fault state, reactive power control during fault, control of the initial moment of fault end, and control of the recovery period after fault end.

[0059] Taking the low / high voltage ride-through characteristics of a static VAR generator (STATCOM) as an example, the specific steps of the parameter setting process are as follows:

[0060] S1. Read the collected information and fill in the control system model (VG), additional control card (VG+), dead zone card (VGQ-DBBD), device constant reactive power card (VGQ-QCON), constant voltage constant reactive power mode switching card (VGQ-QVSW), SVG low voltage ride-through card (VGQ-LVRT), SVG high voltage ride-through card (VGQ-HVRT), and voltage protection card (RE).

[0061] S2. Preliminary identification of parameters. Use MATLAB to read the semi-physical simulation test data, count the reactive power and reactive current before the voltage fault and during the fault steady state, calculate the current recovery rate during the fault recovery period, and verify the consistency of different test data within the same group. If the data is inconsistent, it means that the SVG control device cannot maintain the same response characteristics under the same test conditions, and the test cannot be repeated and reproduced.

[0062] S201, SVG controller model parameters. The voltage threshold for entering and exiting high / low voltage crossing can be identified by the reactive power size under various working conditions. (The reactive power remains unchanged when the SVG is operating normally; when the voltage drops, the SVG emits reactive power; when the voltage rises, the SVG absorbs reactive power)

[0063] S202, voltage ride-through control parameters. By analyzing the data under various working conditions, taking overcurrent protection as a boundary condition, the relationship between reactive current and voltage during the fault voltage ride-through period is identified.

[0064] During the fault ride-through period, the fitting formula of the reactive current command is as follows:

[0065] I t =I eq +K(V ref -V t )

[0066] Among them, I t Indicates the current during the low-breakthrough process; I eq Indicates the average current of the previous N steps at the end of the low-pass delay; K, coefficient; V ref , Installation point reference voltage, the default value is 0.9 for low voltage ride-through and 1.1 for high voltage ride-through; V t , installation point voltage.

[0067] S203, generate SVG model parameters based on the above physical parameters, operating parameters and initial setting control parameter identification results, and write them into the PSD-BPA model card.

[0068] S3. Construct the initial tree group of the TSA search phase, and select the initial voltage, reactive current, reactive power and voltage value of each working condition as the initial point.

[0069] Through the action of the TSA initial tree group, the voltage value of the large power grid arrangement, the voltage and reactive power of the SVG equipment, and the ground fault impedance solution are taken as the initial solution.

[0070] S4. Write the fault voltage ride-through test power flow model corresponding to the SVG model based on PSD-BPA

[0071] S5. Run the S4 program to verify the deviation between the PSD-BPA simulation initial voltage and the initial voltage and initial reactive power of the semi-physical simulation test. If the deviation is not met, adjust the voltage value arranged by the large power grid and the voltage value arranged by SVG and run the S4 program again to limit the BPA simulation voltage and the initial voltage and initial reactive power of the semi-physical simulation test within the deviation range.

[0072] S6. Transient model of fault voltage ride-through test corresponding to BPA-based SVG model

[0073] S7, run S6 program, and verify the deviation between the PSD-BPA simulated fault steady-state voltage and the semi-physical simulation fault steady-state voltage. If the deviation is not satisfied, perform local control parameter optimization through TSA until the PSD-BPA simulated fault steady-state voltage and the semi-physical simulation fault steady-state voltage are limited within the deviation range.

[0074] S8, run S6 program, obtain the reactive power and reactive current of BPA simulation data and semi-physical simulation data under various working conditions, and refer to Table 3 for deviation value verification. If they are not satisfied, optimize through TSA, and the steps are similar to S7.

[0075] S9. Classify all control parameters that meet the deviation value verification through TSA, and finally obtain 1 set of control parameters that complete identification verification, 2 sets of control parameters that complete identification verification, and 4 sets of control parameters that complete identification verification, and output the control parameter results.

[0076] TSA is an intelligent optimization algorithm that simulates the evolution of trees in nature. It mainly consists of three parts: tree species generation, tree species selection, and seed search.

[0077] Tree species generation

[0078] Tree group initialization

[0079] The initial tree group generation method is as follows:

[0080] T i,j =L j,min +r i,j (H j,max -L j,min )

[0081] Among them, i is the tree individual number, j is the dimension, L j,min is the lower bound of the search space of the tree optimization algorithm, H j,max is the upper bound of the tree species optimization search space, r i,j is a constantly changing random number in each dimension and position, r i,j ∈(0,1).

[0082] Tree species generation

[0083] The number of seeds generated for each tree is random, with an upper limit of 0.25N and a lower limit of 0.1N. The newly generated seeds are exactly the same as the current tree.

[0084] Select the best tree species

[0085] Set the fitness function, find the corresponding fitness value, and select the tree individual with the smallest fitness value:

[0086]

[0087] N is the total number of trees.

[0088] Seed Search

[0089] The algorithm has a parameter ST (search tendency), which represents the search tendency of the algorithm. A larger ST means a stronger local search capability and a faster convergence speed; while a smaller ST means a stronger global search capability, ST∈0,1).

[0090] The algorithm provides two strategies for updating seeds, and each seed is randomly selected for update:

[0091] Method 1S i,j =T i,j +α×(B j -T r,j )

[0092] Mode 2S i,j =T i,j +α×(T i,j -T r,j )

[0093] Among them, S i,j represents the jth dimension of the i-th seed number, B j is the optimal tree individual of the jth dimension, T r,j represents the j-th dimension of a randomly selected r-tree.

[0094] TSA is a competitive new swarm intelligence optimization algorithm, which is inspired by the growth and reproduction of trees in nature. Standard TSA has a simpler structure, higher search accuracy and stronger robustness than some traditional intelligent optimization algorithms. It has the advantages of strong global search ability, fewer adjustment parameters, simple and easy implementation. This algorithm has a very obvious feature that when dealing with different dimensions of individuals, the dimensions are independent of each other. At the same time, due to the advantages of strong global search ability, fewer adjustment parameters, simple and easy implementation, it has a good ability to handle the identification of related parameters in SVG modeling built by PSD-BPA.

[0095] The PSD-BPA model setting and verification method based on the tree species optimization algorithm is based on the PSD-BPA platform for electromechanical modeling, combined with the tree species optimization algorithm TSA, to identify parameters for multiple groups of parameters under various fault conditions, and to refer to the deviations specified in the regulations as a verification reference. The results show that the identification results have good compatibility and can meet the deviation requirements. In addition, the present invention is based on the TSA architecture, and is fully compatible with the verification type, data source, type of working condition, and number of working conditions. It can complete the electromechanical parameter identification + model verification of SVG based on test data, and can also complete model verification or data verification based on test data + modeling report. It has a good processing capability for the identification of related parameters in SVG modeling built by PSD-BPA.

[0096] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A PSD-BPA model tuning and verification method based on a tree species optimization algorithm, characterized in that: The following steps are involved: S1: Read the funding information and fill in the model card of the PSD-BPA model; S2: Read the semi-physical simulation test data, perform preliminary parameter identification, generate SVG model parameters, and write them into the PSD-BPA model card; S3: construct the initial tree group of the TSA search phase, select the initial parameters of each working condition as the initial point, and obtain the initial solution; S4: Write a fault voltage ride-through test power flow model corresponding to the SVG model based on PSD-BPA, and verify the deviation between the initial voltage of PSD-BPA simulation and the initial voltage and initial reactive power of the semi-physical simulation test; S5: Write a transient model for fault voltage ride-through test corresponding to the BPA-based SVG model, and verify the deviation between the PSD-BPA simulated fault steady-state voltage and the semi-physical simulated fault steady-state voltage; S6: Based on S5, the reactive power and reactive current of the BPA simulation data and the semi-physical simulation data under each working condition are verified by referring to the regulations for deviation value; S7: Classify all control parameters that meet the deviation value verification through TSA, obtain several sets of control parameters to complete identification verification, and output the control parameter results.

2. The PSD-BPA model setting and verification method based on the tree species optimization algorithm according to claim 1 is characterized in that: In S1, the model cards of the PSD-BPA model include a control system model (VG), an additional control card (VG+), a dead zone card (VGQ-DBBD), a device constant reactive power card (VGQ-QCON), a constant voltage constant reactive power mode switching card (VGQ-QVSW), an SVG low voltage ride-through card (VGQ-LVRT), an SVG high voltage ride-through card (VGQ-HVRT), and a voltage protection card (RE).

3. The PSD-BPA model setting and verification method based on the tree species optimization algorithm according to claim 1, characterized in that: In S2, the semi-physical simulation test data is read through MATLAB, the reactive power and reactive current before the voltage fault and during the fault steady state are counted, the current recovery rate during the fault recovery period is calculated, and the consistency of different test data in the same group is verified. When the data is inconsistent, it indicates that the SVG control device cannot maintain the same response characteristics under the same test conditions, and the test cannot be repeated and reproduced.

4. The PSD-BPA model setting and verification method based on the tree species optimization algorithm according to claim 3 is characterized in that: The S2 specifically includes the following steps: S21: SVG controller model parameters, which identifies the voltage threshold for entering and exiting high / low voltage ride-through through the reactive power size under various working conditions; S22: Voltage ride-through control parameters. By analyzing the data under various working conditions and taking overcurrent protection as the boundary condition, the relationship between reactive current and voltage during the fault voltage ride-through period is identified. During fault ride-through, the fitting formula of reactive current command is as follows: I t =I eq +K(V ref -V t ) Among them, I t Indicates the current during the low-breakthrough process; I eq Indicates the average current value of the previous N steps when the low-pass delay ends; K, coefficient; V ref , Installation point reference voltage, the default value is 0.9 for low voltage ride-through and 1.1 for high voltage ride-through; V t , installation point voltage; S23: Generate SVG model parameters based on the identification results of the above physical parameters, operating parameters and initially set control parameters, and write them into the PSD-BPA model card.

5. The PSD-BPA model tuning and verification method based on the tree species optimization algorithm according to claim 1, characterized in that: In S3, the initial tree group of the TSA search phase is constructed, and the initial voltage, reactive current, reactive power and voltage value of each operating condition are selected as the initial points. Through the action of the TSA initial tree group, the voltage value of the large power grid arrangement, the voltage and reactive power of the SVG equipment and the solution of the ground fault impedance are obtained as the initial solution.

6. The PSD-BPA model tuning and verification method based on the tree species optimization algorithm according to claim 5 is characterized in that: In S4, when the deviation is verified, if the deviation is not met, the voltage value arranged by the large power grid and the voltage value arranged by the SVG are adjusted, and the fault voltage ride-through test flow model is run again to limit the BPA simulation voltage and the initial voltage and actual reactive power of the semi-physical simulation test within the deviation range.

7. The PSD-BPA model tuning and verification method based on the tree species optimization algorithm according to claim 6 is characterized in that: In S5, when the deviation is not satisfied during the deviation verification, local control parameter optimization is performed through TSA until the PSD-BPA simulated fault steady-state voltage and the semi-physical simulation fault steady-state voltage are limited within the deviation range.

8. The PSD-BPA model tuning and verification method based on the tree species optimization algorithm according to claim 7 is characterized in that: In S6, when the deviation value is verified, if it is not satisfied, optimization is performed through TSA until the reactive power and reactive current of the BPA simulation data and the semi-physical simulation data under each working condition are limited within the deviation value range.

9. The PSD-BPA model tuning and verification method based on the tree species optimization algorithm according to claim 8, characterized in that: In S7, all control parameters that meet the deviation value verification are classified to obtain 1 set of control parameters that complete identification verification, 2 sets of control parameters that complete identification verification, and 4 sets of control parameters that complete identification verification, and the control parameter results are output.

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