Method for setting and verifying fan unit model based on Harris eagle optimization algorithm

Abstract

The invention provides a method for setting and verifying a fan unit model based on a Harris eagle optimization algorithm, which relates to the technical field of electric power analysis, and comprises the following steps: reading information collection data, and determining the model type of a fan unit, generator basic parameters, grid-related protection constant value parameters, wind power parameters, wind wheel parameters and torque control; reading test data in the information collection data, and generating a simulation power flow parameter set based on the initial time of each working condition in the test data through a Harris eagle algorithm compiled by Python; based on the Harris eagle algorithm architecture, the method is compatible with verification types, data sources, working condition types and working condition number, electromechanical parameter identification and model verification of the fan unit can be completed according to test data, and model verification or data verification can be completed according to test data and a modeling report. And for various high-power, low-power, symmetric and asymmetric working conditions, multi-group parameter identification simulation verification can be realized, and the optimal solution can be completed and achieved at one time.

Description

Technical Field

[0001] The present invention relates to the technical field of power analysis, and in particular to a method for setting and verifying a wind turbine unit model based on a Harris Eagle optimization algorithm. Background Art

[0002] Based on several regulations on power secondary system safety management, various regions are further standardizing the grid-connected commissioning and grid-related performance testing and evaluation of new energy and energy storage power stations to improve their grid-connected performance. Therefore, it is necessary to conduct electromechanical parameter identification and simulation verification of wind turbine unit performance at new energy stations.

[0003] The Power System Analysis Software Package (PSASP) is abbreviated as PSASP. Simulation and verification of the electromechanical model of a wind turbine unit is mainly completed within PSASP. Parameters need to be identified from a huge amount of test data. The current solution is generally manual operation, which is tedious and time-consuming. In actual engineering projects, the methods and approaches for wind turbine unit simulation verification, data sources, and the knowledge of the responsible persons are inconsistent, resulting in deviations in the implementation of simulation verification. The parameter identification results only meet the deviation value verification and are not optimal. The operating conditions are not fully compatible and do not solve the actual problem. Therefore, the present invention proposes a method for wind turbine unit model tuning and verification based on the Harris Eagle optimization algorithm to solve the problems existing in the prior art. Summary of the Invention

[0004] In response to the above problems, the present invention proposes a method for adjusting and verifying a fan unit model based on the Harris Eagle optimization algorithm. The method for adjusting and verifying a fan unit model based on the Harris Eagle optimization algorithm is based on the Harris Eagle algorithm architecture, and is compatible with verification types, data sources, types of operating conditions, and numbers of operating conditions. It can complete the electromechanical parameter identification + model verification of the fan unit based on test data, and can also complete model verification or data verification based on test data + modeling report.

[0005] To achieve the purpose of the present invention, the present invention is implemented by the following technical solution: a method for wind turbine unit model tuning and verification based on Harris Eagle optimization algorithm, comprising the following steps:

[0006] S1: Read the collected data, determine that the wind turbine unit adopts the double-fed wind turbine model, fill in the model card, and perform preliminary parameter identification;

[0007] S2: The physical parameters, operating parameters, and initially set control parameters are used to read the simulation power flow parameter set of the initial active power and reactive power of each working condition in the semi-physical simulation data through the Harris Eagle algorithm written in MATLAB;

[0008] S3: Read the test data in the collected data, and generate a simulation transient parameter set based on the initial voltage of each working condition drop / rise in the test data using the Harris Eagle algorithm written in MATLAB;

[0009] S4: Based on the PSASP wind turbine model, a fault voltage ride-through test model is constructed to simulate the rise and fall of the collector line voltage;

[0010] S5: Use Navicat Premium to write the simulated power flow parameter set and the simulated transient parameter set into the power flow database and transient database of PSASP respectively for simulation and deviation verification;

[0011] S6: Verify the deviation values ​​of the simulation data under each working condition and the test data of the collected data before, during and after the fault in the transient and steady-state intervals according to industry standards;

[0012] S7: Use the Harris Eagle algorithm to classify the current control methods that meet the deviation value verification, and make comprehensive trade-offs and iterative adjustments to the characteristic control parameters;

[0013] S8: Complete identification and verification according to the optimal control parameters, complete identification and verification for multiple sets of control parameters, and output the control parameter results.

[0014] 2. The method for wind turbine model tuning and verification based on the Harris Eagle optimization algorithm according to claim 1 is characterized in that: in S1, the doubly fed generator model (MM), wind turbine shaft system model (ME), wind power model (TG), active and reactive current limitation (ES), fault ride-through fault judgment (EV), pitch angle control (GF), active power control under normal state (EP), and reactive power control under normal state (EZ) model cards are filled in.

[0015] 3. The method for wind turbine model tuning and verification based on the Harris Eagle optimization algorithm according to claim 2, characterized in that: in said S1, semi-physical simulation test data is read through MATLAB, the active power, reactive power, reactive current, and active current before the voltage fault and during the fault steady state are statistically calculated, the current recovery rate during the fault recovery period is calculated, and the consistency of different test data within a unified group is verified. If the data is inconsistent, it indicates that the inverter 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:

[0016] S11: Identify the voltage threshold for entering and exiting high / low voltage ride-through based on the reactive power under various working conditions;

[0017] S12: By analyzing the data under various operating conditions and considering overcurrent protection as a boundary condition, the relationship between reactive current or active current and voltage during the fault voltage ride-through period is parameter identified;

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

[0019] I qLVRT =K 1IqLV (U Lin -U t )+K 2IqLV I q0 +I qsetLV

[0020] Where K 1IqLV ——Reactive current calculation coefficient 1;

[0021] K 2IqLV ——Reactive current calculation coefficient 2;

[0022] I qsetLV ——Reactive current setting value 3;

[0023] I q0 ——initial reactive current;

[0024] U t ——terminal voltage amplitude;

[0025] U Lin ——Entering the low voltage ride-through threshold.

[0026] Fitting formula of active current command:

[0027] I pLVRT =K 1IpLV U t +K 2IpLV I p0 +I psetLV

[0028] S13: Generate a fan unit parameter set based on the above physical parameters, operating parameters and initial setting control parameter identification results, and write it into the PSD-BPA model card.

[0029] A further improvement is that: said S4 includes the following steps:

[0030] S41: Connect the wind turbine group T_Gen to the power grid T_GRID through the step-up transformer T_TRAN and the collector line T_AC in sequence;

[0031] S42: Set the data measurement point on the high-voltage side of the step-up transformer and the fault point on the collector line at a position 10% of the distance from the low-voltage side of the collector line;

[0032] S43: A function of simulating a fault point is formed in the PSASP to simulate the rise and fall of the collector line voltage to obtain a test model for fault voltage ride-through.

[0033] A further improvement is that in S5, the simulated power flow data set is written into the power flow database of PSASP through Navicat Premium, simulation is performed through the power flow calculation function of PSASP, and the deviation between the simulated power flow value and the initial value of the test data is verified. When the deviation is not satisfied, the transformer ratio is adjusted and iterative calculation is performed to control the simulated power flow value and the initial value of the test data to reach the error range.

[0034] Further improvements are as follows: in S5, the simulated transient data set is written into the transient database of PSASP through Navicat Premium, simulation is performed through the transient calculation function of PSASP, and the deviation between the simulated fault voltage drop / rise value and the test data fault voltage rise / drop value is verified. When the deviation is not met, local optimization is performed through the Harris Eagle algorithm, and the collector line impedance is adjusted and iterative calculation is performed to control the consistency of the true fault voltage drop / rise value and the test data fault voltage rise / drop value to achieve the current optimal solution.

[0035] A further improvement is that: S7 includes the following steps:

[0036] S71: The Harris Eagle algorithm is used to classify the current control modes that meet the deviation value verification into high power, low power, symmetrical and asymmetrical;

[0037] S72: Comprehensively weigh and iteratively adjust the control parameters of the high and low voltage ride-through process during fault period, the characteristics of the fault recovery initial stage, and the characteristics during the fault recovery period.

[0038] A further improvement is that in S8, the multiple sets of control parameters completing identification and verification include: one set of control parameters completing identification and verification, two sets of control parameters completing identification and verification, and four sets of control parameters completing identification and verification.

[0039] The beneficial effects of the present invention are:

[0040] 1. The present invention is based on the algorithm architecture of Harris Eagle 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 the wind turbine unit based on test data, and can also complete model verification or data verification based on test data + modeling report. In actual work, it can solve the above problems at a high speed. For various high-power, low-power, symmetrical, and asymmetrical working conditions, multiple groups of parameter identification simulation verification can be completed in one go and the optimal solution can be achieved.

[0041] 2. The present invention adopts the Harris Eagle algorithm, which has the advantages of strong global search capability, few adjustment parameters, simple principle and easy implementation. It has a good processing ability for the identification problem of relevant parameters in the wind farm modeling built by PSASP, and plays an important role in the electromechanical parameter setting of wind turbine units. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a principle flow chart of the present invention;

[0043] Figure 2 Schematic diagram of a fault voltage ride-through test model of the present invention;

[0044] Figure 3 A schematic diagram of the maximum allowable value of the electromechanical transient model verification deviation of the fault ride-through characteristic of the present invention;

[0045] Figure 4 It is the algorithm flow chart of the present invention. DETAILED DESCRIPTION

[0046] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to the examples. The examples are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0047] Example 1

[0048] according to Figure 1 、 2 As shown in , 3, and 4, this embodiment proposes a method for wind turbine unit model tuning and verification based on the Harris Eagle optimization algorithm, including the following steps:

[0049] S1: Read the collected data, determine that the wind turbine unit adopts the doubly fed wind turbine model, fill in the model card, and perform preliminary parameter identification; fill in the doubly fed generator model (MM), wind turbine shaft system model (ME), wind power model (TG), active and reactive current limit (ES), fault ride-through fault judgment (EV), pitch angle control (GF), active power control under normal state (EP), and reactive power control under normal state (EZ) model cards; read the semi-physical simulation test data through MATLAB, and statistically calculate the active power, reactive power, reactive current, and active 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. If the data is inconsistent, it means that the inverter 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: identify the voltage threshold value for entering and exiting high / low voltage ride-through through the size of reactive power under each working condition; by analyzing the data under each working condition, considering overcurrent protection as a boundary condition, perform parameter identification on the relationship between reactive current or active current and voltage during the fault voltage ride-through period;

[0050] During the fault ride-through period, the fitting formula of the reactive current command is shown as follows:

[0051] I qLVRT =K 1IqLV (U Lin -U t )+K 2IqLV I q0 +I qsetLV

[0052] Where K 1IqLV ——Reactive current calculation coefficient 1;

[0053] K 2IqLV ——Reactive current calculation coefficient 2;

[0054] I qsetLV ——Reactive current setting value 3;

[0055] I q0 ——initial reactive current;

[0056] U t ——terminal voltage amplitude;

[0057] U Lin ——Entering the low voltage ride-through threshold.

[0058] Fitting formula of active current command:

[0059] I pLVRT =K 1IpLV U t +K 2IpLV I p0 +I psetLV

[0060] The above physical parameters, operating parameters and initial adjustment control parameter identification results are used to generate the fan unit parameter set and written into the PSD-BPA model card.

[0061] S2: The physical parameters, operating parameters, and initially set control parameters are used to read the simulation power flow parameter set of the initial active power and reactive power of each working condition in the semi-physical simulation data through the Harris Eagle algorithm written in MATLAB;

[0062] S3: Read the test data in the collected data, and generate a simulation transient parameter set based on the initial voltage of each working condition drop / rise in the test data using the Harris Eagle algorithm written in MATLAB;

[0063] S4: Based on the wind turbine unit model of PSASP, a fault voltage ride-through test model is constructed to simulate the rise and fall of the collector line voltage; specifically, the following steps are included: the wind turbine unit T_Gen is connected to the power grid T_GRID through the step-up transformer T_TRAN and the collector line T_AC in sequence; the data measurement point is set on the high-voltage side of the step-up transformer, and the fault point is set at a position 10% of the distance from the collector line to the low-voltage side of the collector line; a function of simulating the fault point is formed in PSASP to simulate the rise and fall of the collector line voltage to obtain a test model for fault voltage ride-through.

[0064] S5: Use Navicat Premium to write the simulation power flow parameter set and the simulation transient parameter set into the power flow database and transient database of PSASP respectively for simulation and deviation verification; use Navicat Premium to write the simulation power flow data set into the power flow database of PSASP, use the power flow calculation function of PSASP to simulate, and verify the deviation between the simulation power flow value and the initial value of the test data. If the deviation is not satisfied, adjust the transformer ratio and perform iterative calculation to control the simulation power flow value and the initial value of the test data to reach the error range; use Navicat Premium to write the simulation transient data set into the transient database of PSASP, use the transient calculation function of PSASP to simulate, and verify the deviation between the simulation fault voltage drop / rise value and the test data fault voltage rise / drop value. If the deviation is not satisfied, perform local optimization through the Harris Eagle algorithm, adjust the collector line impedance and perform iterative calculation to control the consistency between the true fault voltage drop / rise value and the test data fault voltage rise / drop value to achieve the current optimal solution.

[0065] S6: Verify the deviation values ​​of the simulation data under each working condition and the test data of the collected data before, during and after the fault in the transient and steady-state intervals according to industry standards;

[0066] S7: Classify the current control methods that meet the deviation value verification through the Harris Eagle algorithm, and make comprehensive trade-offs and iterative adjustments to the characteristic control parameters; specifically, the following steps are included: Classify the current control methods that meet the deviation value verification into high power, low power, symmetrical and asymmetrical through the Harris Eagle algorithm; Make comprehensive trade-offs and iterative adjustments to the characteristics during the high and low voltage ride-through process fault, the characteristics of the fault recovery initial stage, and the characteristic control parameters during the fault recovery period.

[0067] S8: completing identification verification according to the optimal control parameters and completing identification verification for multiple sets of control parameters, and outputting the control parameter results. Completing identification verification for multiple sets of control parameters includes: completing identification verification for one set of control parameters, completing identification verification for two sets of control parameters, and completing identification verification for four sets of control parameters.

[0068] Example 2

[0069] according to Figure 1 、 2 As shown in , 3, and 4, this embodiment proposes a method for wind turbine unit model tuning and verification based on the Harris Eagle optimization algorithm, including the following steps:

[0070] Wind turbine unit electromechanical simulation model parameters are generally categorized into physical parameters, operating parameters, and control parameters. Physical and operating parameters can be directly obtained from the data collection table, while control parameters are the primary identification parameters. Typically, key identification parameters include those for four key areas: fault state determination, active and reactive power control during the fault period, initial fault end time control, and fault end recovery period control.

[0071] Taking the low voltage ride-through characteristics of a doubly-fed wind turbine as an example, the specific steps are as follows:

[0072] S1: Read the collected information, determine that the wind turbine unit adopts the doubly fed wind turbine model, and fill in the doubly fed generator model (MM), wind turbine unit shaft model (ME), wind power model (TG), active and reactive current limit (ES), fault ride-through fault judgment (EV), pitch angle control (GF), active power control under normal state (EP), and reactive power control under normal state (EZ) model cards.

[0073] S2: Preliminary parameter identification. Using MATLAB, read the semi-physical simulation test data and calculate the active power, reactive power, reactive current, and active current before the voltage fault and during the steady-state period. Calculate the current recovery rate during the fault recovery period. Verify the consistency of test data across different runs within the same group. Inconsistency indicates that the inverter cannot maintain consistent response characteristics under the same test conditions, making the test unrepeatable and unreproducible.

[0074] Doubly-fed wind turbine converter model parameters. The reactive power under various operating conditions can be used to identify the voltage thresholds for high / low voltage ride-through.

[0075] Voltage ride-through control parameters. By analyzing data under various operating conditions and considering overcurrent protection as a boundary condition, the relationship between reactive current or active current and voltage during the fault voltage ride-through period is identified.

[0076] During the fault ride-through period, the fitting formula of the reactive current command is shown as follows:

[0077] I qLVRT =K 1IqLV (U Lin -U t )+K 2IqLV I q0 +I qsetLV

[0078] Where K 1IqLV ——Reactive current calculation coefficient 1;

[0079] K 2IqLV ——Reactive current calculation coefficient 2;

[0080] I qsetLV ——Reactive current setting value 3;

[0081] I q0 ——initial reactive current;

[0082] U t ——terminal voltage amplitude;

[0083] U Lin ——Entering the low voltage ride-through threshold.

[0084] Fitting formula of active current command:

[0085] I pLVRT =K 1IpLV U t +K 2IpLV I p0 +I psetLV

[0086] The above physical parameters, operating parameters and initial adjustment control parameter identification results are used to generate the fan unit parameter set and written into the PSD-BPA model card.

[0087] S3: The physical parameters, operating parameters and initially set control parameters are used to read the simulation flow parameter set of the initial active power and reactive power of each working condition in the semi-physical simulation data through the Harris Eagle algorithm written in MATLAB.

[0088] S4: Read the test data in the collected information, and generate a simulation transient parameter set based on the initial voltage during the drop / rise of each working condition in the test data through the Harris Eagle algorithm written in MATLAB.

[0089] S5: Fault voltage ride-through test model for a wind turbine unit model based on the PSASP: The wind turbine unit (T_Gen) is connected to the grid (T_GRID) via a step-up transformer (T_TRAN) and a collector line (T_AC). The data measurement point is located on the high-voltage side of the step-up transformer, and the fault point is located 10% of the distance from the collector line to the low-voltage side of the collector line. By setting up a fault point simulation function within the PSASP, the rise and fall of the collector line voltage is simulated to obtain a fault voltage ride-through test model.

[0090] S6: Use Navicat Premium to write the power flow dataset of S3 into the power flow database of PSASP. Use the power flow calculation function of PSASP to perform simulation and verify the deviation between the simulated power flow U and the initial U of the test data. If the deviation is not satisfied, adjust the transformer ratio and perform iterative calculations to ensure that the simulated power flow U and the initial U of the test data are within the error range.

[0091] S7: Use Navicat Premium to write the transient data set of S4 into the transient database of PSASP. Use the transient calculation function of PSASP to perform simulation and verify the deviation between the simulated fault voltage drop / rise U and the test data fault voltage rise / drop U. If the deviation is not satisfied, use the Harris Eagle algorithm for local optimization, adjust the collector line impedance, and then perform iterative calculations to ensure the consistency of the real fault voltage drop / rise U and the test data fault voltage rise / drop U to achieve the current optimal solution.

[0092] S8: Use S7 to obtain the simulation data under each working condition and the test data of the collected data to verify the deviation values ​​of the transient interval and steady-state interval before, during and after the fault according to the specifications.

[0093] S9: The Harris Eagle algorithm is used to classify the current control methods that meet the deviation value verification into high power, low power, symmetrical and asymmetrical, and the control parameters of the high and low voltage ride-through process during the fault period, the characteristics of the fault recovery initial stage, and the characteristics during the fault recovery period are comprehensively weighed and iteratively adjusted.

[0094] S10: completing identification and verification according to the optimal control parameters, completing identification and verification according to one set of control parameters, completing identification and verification according to two sets of control parameters, and completing identification and verification according to four sets of control parameters, and outputting control parameter results.

[0095] Harris Hawk Algorithm Principle

[0096] The Harris Hawk Optimization Algorithm is an intelligent optimization algorithm that simulates the predation behavior of a Harris Hawk. It mainly consists of three parts: the search phase, the conversion between search and development, and the development phase.

[0097] Search phase

[0098] Harris's Hawks roost randomly and find their prey using two strategies:

[0099] In the HHO algorithm, each Harris hawk is a candidate solution, and each has a chance to approach the target value. During the exploration phase, all Harris hawks randomly perch on power poles or trees to monitor and observe their prey. Two equally probabilistic methods are used for monitoring, and the mathematical model is as follows:

[0100]

[0101] Among them, X(t) represents the current iteration individual, X(t+1) represents the next iteration individual, Xrand(t) represents a random individual in the population, Xrabbit(t) represents the position of the rabbit, that is, the position of the prey, Xm(t) represents the average position of all individuals in the current evolving population, ub, lb represent the upper and lower boundary values ​​of the eagle's position, q, r1, r2, r3, r4∈(0, 1), and N represents the population size.

[0102] Transition from exploration to development

[0103] Harris's hawks employ different strategies to capture their prey based on their flight patterns. This complex and varied escape pattern is determined by the prey's escape energy factor, which determines whether the HHO algorithm performs a global or local search.

[0104]

[0105] E0=2r5-1 (4)

[0106] Where E is the escape energy factor, E0∈(-1, 1) represents the energy that the prey has at the beginning, t is the current number of iterations, T is the maximum number of iterations, and r5∈(0, 1).

[0107] Development Stage

[0108] During the exploration phase, Harris's hawks roam the search space, observing and monitoring prey behavior, waiting for an opportunity to capture them. During the exploration phase, Harris's hawks attack prey using four strategies. When |E| ≥ 1, the algorithm enters the exploration phase. When |E| < 1, the algorithm enters the exploitation phase, using a random number r∈(0, 1) to represent the probability of prey escape.

[0109] Use soft siege to surround prey

[0110] When r ≥ 0.5 && |E| ≥ 0.5, the rabbit has enough energy to escape and tries to jump, but ultimately fails. During the rabbit's escape, the eagle gently surrounds it, exhausting it before catching it. This behavior is modeled by the following mathematical model:

[0111] X(t+1)=ΔX(t)-EJX rabbit (t)-X(t)|(5)

[0112] ΔX(t)=X rabbit (t)-X(t)(6)

[0113] J=2(1-r6)(7)

[0114] Where r6∈(0,1), △X(t) represents the position difference between the current rabbit position and the eagle position, and J represents the jumping intensity of the rabbit. Different values ​​of J are used to simulate the escape behavior of the rabbit.

[0115] Use hard siege to surround the prey

[0116] When r≥0.5&&|E|<0.5, the rabbit does not have enough energy to escape and is exhausted, so the eagle directly captures the rabbit. This is simulated using the following formula:

[0117] X(t+1)=X rabbit (t)-EX(t)|(8)

[0118] Use gradual dives to softly encircle and surround prey

[0119] When r < 0.5 &|E| ≥ 0.5, the rabbit has the energy to escape, but the eagle adopts a more flexible and resourceful strategy. First, the concept of levy flight is introduced to mimic the rabbit's deceptive maneuvers before escaping. The eagle will perform several irregular, rapid dives around the rabbit, attempting to correct its position and direction. Second, a greedy selection mechanism is introduced at this stage, comparing the previous movement with the results of the levy flight movement and selecting the more optimal one. The mathematical model is as follows:

[0120] Y=X rabbit (t)-EJX rabbit (t)-X(t)|(9)

[0121] Z=Y+S×LF(D)(10)

[0122] Where D is the dimension of the problem to be solved, S is a random vector of size 1×D, and LF is the levy flight function.

[0123]

[0124] Where μ, v∈(0, 1), β is a constant value of 1.5.

[0125]

[0126] Where Y and Z are calculated by equations (9) and (10).

[0127] Use gradual rapid diving to hard siege and besiege prey

[0128] When r < 0.5 && |E| < 0.5, the rabbit has insufficient energy to escape. The eagle has formed a circle around the rabbit, using a similar method to a soft, progressive dive trap, also incorporating a levy flight strategy. However, by calculating the average position of the rabbit and the eagle, the optimal position from the rabbit can be better estimated. The mathematical model is as follows:

[0129] Y=X rabbit (t)-EJX rabbit (t)-X m (t)|(13)

[0130] Z=Y+S×LF(D)(14)

[0131]

[0132] Xm(t) is calculated by formula (2).

[0133] Harris Hawk Optimization (HHO) is a competitive new swarm intelligence optimization algorithm inspired by the capture methods used by Harris Hawks during their pursuit of prey. Because prey can escape in different ways, the clever Harris Hawks employ different strategies to capture and siege their prey based on their escape methods. During the attack, the prey's escape ability is correlated with its energy. Therefore, an escape energy factor is used to represent this ability. Modeling the entire process can be divided into two phases: exploration and exploitation. The transition from exploration to exploitation is controlled by the escape energy factor.

[0134] The method for wind turbine unit model setting and verification based on the Harris Eagle optimization algorithm is based on the algorithm architecture of the Harris Eagle, and is fully compatible with verification types, data sources, types of working conditions, and the number of working conditions. It can complete the electromechanical parameter identification + model verification of the wind turbine unit based on test data, and can also complete model verification or data verification based on test data + modeling report. In actual work, while solving the above problems, it can be completed at high speed. For various high-power, low-power, symmetrical, and asymmetrical working conditions, multiple groups of parameter identification simulation verification can be completed in one go and the optimal solution is achieved. At the same time, the present invention adopts the Harris Eagle algorithm, which has the advantages of strong global search capability, few adjustment parameters, and simple and easy-to-implement principles. It has a good processing capability for the identification problem of relevant parameters in the wind farm modeling built by PSASP, and plays an important role in the electromechanical parameter setting of the wind turbine unit.

[0135] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. Method for setting and verifying wind turbine unit model based on Harris Eagle optimization algorithm, It is characterized in that The following steps are involved: S1: Read the collected data, determine that the wind turbine unit adopts the double-fed wind turbine model, fill in the model card, and perform preliminary parameter identification; S2: The physical parameters, operating parameters and initially set control parameters are read through the Harris Eagle algorithm written in MATLAB to read the simulation power flow parameter set of initial active power and reactive power of each working condition in the semi-physical simulation data; S3: Read the test data in the collection data, and generate a simulation transient parameter set based on the initial voltage drop / rise of each working condition in the test data through the Harris Eagle algorithm written in MATLAB; S4: Based on the wind turbine unit model of PSASP, a fault voltage ride-through test model is constructed to simulate the rise and fall of the collector line voltage; S5: Use Navicat Premium to write the simulation power flow parameter set and simulation transient parameter set into the power flow database and transient database of PSASP respectively for simulation and deviation verification; S6: Verify the deviation values ​​of the transient interval and steady-state interval of the simulation data and the test data of the collected data before, during and after the fault according to the industry specifications; S7: Classify the current control methods that meet the deviation value verification through the Harris Eagle algorithm, and make comprehensive trade-offs and iterative adjustments to the characteristic control parameters; S8: Complete identification and verification according to the optimal control parameters, complete identification and verification of multiple sets of control parameters, and output the control parameter results.

2. The method for wind turbine unit model setting and verification based on Harris Eagle optimization algorithm according to claim 1, Features: In the S1, fill in the doubly-fed generator model (MM), wind turbine shaft system model (ME), wind power model (TG), active and reactive current limitation (ES), fault ride-through fault judgment (EV), pitch angle control (GF), active control under normal state (EP), and reactive control under normal state (EZ) model cards.

3. The method for wind turbine unit model setting and verification based on Harris Eagle optimization algorithm according to claim 2, Features: In the S1, the semi-physical simulation test data is read through MATLAB, the active power, reactive power, reactive current, and active 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 checked at the same time. When the data is inconsistent, it indicates that the inverter 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: S11: Identify the voltage threshold value of entering and exiting high / low voltage ride-through through the reactive power size under various working conditions; S12: By analyzing the data under various working conditions, taking overcurrent protection as a boundary condition, the relationship between reactive current or active current and voltage during the fault voltage ride-through period is identified; During the fault ride-through period, the fitting formula of the reactive current command is as follows: AND qLVRT =K 1IqLV (IN Lin -IN t )+K 2IqLV AND q0 +I qsetLV Where K 1IqLV ——Reactive current calculation coefficient 1; K 2IqLV ——Reactive current calculation coefficient 2; I qsetLV ——Reactive current setting value 3; I q0 ——initial reactive current; U t ——Terminal voltage amplitude; U Lin ——Entering the low voltage ride-through threshold. Fitting formula of active current command: I pLVRT =K 1IpLV U t +K 2IpLV I p0 +I psetLV S13: Generate a fan unit parameter set based on the above physical parameters, operating parameters and initial setting control parameter identification results, and write it into the PSD-BPA model card.

4. The method for wind turbine unit model tuning and verification based on Harris Eagle optimization algorithm according to claim 1, Features: The S4 comprises the following steps: S41: Connect the wind turbine unit T_Gen to the power grid T_GRID through the step-up transformer T_TRAN and the collector line T_AC in sequence; S42: setting the data measurement point at the high voltage side of the step-up transformer and setting the fault point at a position where the collector line is 10% away from the low voltage side of the collector line; S43: A function of simulating a fault point is formed in the PSASP to simulate the rise and fall of the collector line voltage and obtain a test model of fault voltage ride-through.

5. The method for wind turbine unit model tuning and verification based on Harris Eagle optimization algorithm according to claim 1, Features: In S5, the simulated power flow data set is written into the power flow database of PSASP through Navicat Premium, and the simulation is performed through the power flow calculation function of PSASP. The deviation between the simulated power flow value and the initial value of the test data is verified. When the deviation is not satisfied, the transformer ratio is adjusted and iterative calculation is performed to control the simulated power flow value and the initial value of the test data to reach the error range.

6. The method for wind turbine unit model tuning and verification based on Harris Eagle optimization algorithm according to claim 5, Features: In S5, the simulated transient data set is written into the transient database of PSASP through Navicat Premium, and simulation is performed through the transient calculation function of PSASP. The deviation between the simulated fault voltage drop / rise value and the test data fault voltage rise / drop value is verified. When the deviation is not met, local optimization is performed through the Harris Eagle algorithm, and the collector line impedance is adjusted and iterative calculation is performed to control the consistency between the true fault voltage drop / rise value and the test data fault voltage rise / drop value to achieve the current optimal solution.

7. The method for wind turbine unit model tuning and verification based on Harris Eagle optimization algorithm according to claim 1, Features: The S7 comprises the following steps: S71: The current control modes that meet the deviation value verification are classified into high power, low power, symmetrical and asymmetrical by Harris Eagle algorithm; S72: Comprehensively weigh and iteratively adjust the control parameters of the characteristics during the high and low voltage ride-through process faults, the characteristics of the initial stage of fault recovery, and the characteristics during the fault recovery period.

8. The method for wind turbine unit model tuning and verification based on Harris Eagle optimization algorithm according to claim 1, Features: In S8, the multiple sets of control parameters completing identification and verification include: one set of control parameters completing identification and verification, two sets of control parameters completing identification and verification, and four sets of control parameters completing identification and verification.

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