MMC-STATCOM parameter identification method of improved whale algorithm based on multi-strategy fusion

Abstract

The invention discloses an MMC-STATCOM parameter identification method of an improved whale algorithm based on multi-strategy fusion, and the method comprises the following steps: S1, analyzing the control principle of an MMC-STATCOM, building a mathematical model of the MMC-STATCOM, and determining parameters needing to be identified; s2, constructing a multi-strategy fusion improved whale algorithm, and carrying out a semi-physical test on the MMC-STATCOM to obtain semi-physical test data; s3, identifying the control parameters of the MMC-STATCOM by using a multi-strategy fusion improved whale algorithm, and analyzing the error of the control parameters of the MMC-STATCOM; according to the MMC-STATCOM control parameter identification method, the precision and the convergence speed of the algorithm are improved, and accurate identification of MMC-STATCOM control parameters is realized.

Description

Technical Field

[0001] The present invention relates to the field of MMC-STATCOM parameter identification, and particularly to an MMC-STATCOM parameter identification method based on an improved whale algorithm with multi-strategy fusion. Background Technique

[0002] In recent years, with the wide application of high-power power electronic devices in the fields of new energy grid connection, high-voltage direct current transmission, and industrial drive, power quality problems such as power grid harmonic pollution, voltage fluctuation, and reactive power imbalance have become increasingly prominent. As one of the core devices of the flexible alternating current transmission system (FACTS), the static synchronous compensator (STATCOM) has become a key device for improving the power quality of the power grid and enhancing system stability due to its significant advantages such as strong dynamic reactive power compensation ability and low harmonic content. Among them, the STATCOM based on the modular multilevel converter (MMC) topology has gradually become the mainstream solution in the medium and high voltage fields due to the technical advantages brought by its modular design, such as high scalability and excellent output voltage harmonic characteristics. However, the actual operating characteristics of the STATCOM device are closely related to the accuracy of its mathematical model parameters. Especially in the new energy high-penetration power grid, the interaction between the STATCOM and power electronic devices such as wind turbine converters and photovoltaic inverters is more complex, which poses higher requirements for the accurate modeling of device parameters. Therefore, for the MMC-STATCOM, which is a mainstream topology, studying its efficient and reliable parameter identification method not only helps to improve the accuracy of power system simulation analysis, but also provides theoretical support for the optimal configuration and coordinated control of the STATCOM, and has important engineering practical value. However, the accuracy of the existing MMC-STATCOM parameter identification methods is generally not high and cannot meet the high-precision modeling requirements of the MMC-STATCOM. Therefore, how to achieve high-precision MMC-STATCOM parameter identification is a key problem that urgently needs to be solved in the analysis of the grid-connected stability of the new energy high-penetration power grid. Summary of the Invention

[0003] The purpose of the present invention is to overcome the above deficiencies and provide an MMC-STATCOM parameter identification method based on an improved whale algorithm with multi-strategy fusion to improve the accuracy and convergence speed of the algorithm and achieve accurate identification of the MMC-STATCOM control parameters.

[0004] To solve the above technical problems, the technical solution adopted by the present invention is: an MMC-STATCOM parameter identification method based on an improved whale algorithm with multi-strategy fusion, including the following steps:

[0005] Step S1: Analyze the control principle of MMC-STATCOM and establish its mathematical model to clarify the parameters to be identified;

[0006] Step S2: Construct an improved whale algorithm with multi-strategy fusion and conduct a hardware-in-the-loop test on MMC-STATCOM to obtain hardware-in-the-loop test data;

[0007] Step S3: Identify the control parameters of MMC-STATCOM using the improved whale algorithm with multi-strategy fusion and analyze its error.

[0008] Preferably, the said Step S1 includes:

[0009] Step S11: Establish the topological structure of MMC-STATCOM based on a voltage-source inverter;

[0010] The entire MMC-STATCOM device is equivalent to a voltage source whose voltage magnitude can be controlled. Let the voltage generated by the MMC-STATCOM device be U C , the injected system current be I, the system voltage be U S , the connecting reactance be L, and the DC voltage of the converter be E. Then the current absorbed by the device is:

[0011]

[0012] The absorbed apparent power S is:

[0013]

[0014] The absorbed reactive power Q is:

[0015]

[0016] In the formula, j is the imaginary unit; is the conjugate value of the injected current; are respectively the conjugate values of the system voltage U S and the terminal voltage U of MMC-STATCOM C ;

[0017] Step S12: Based on the analysis in S11, the mathematical expression of the MMC-STATCOM control model is as follows:

[0018]

[0019] In the formula: K p1 , K i1 are respectively the proportional and integral coefficients of the DC voltage outer loop; K p2 , K i2 are respectively the proportional and integral coefficients of the AC current outer loop; K p3, K i3 are the proportional and integral coefficients of the DC current inner loop respectively; U dc is the DC voltage value; U dcref is the DC voltage reference value; U d , U q are the d-axis and q-axis components of the voltage control signal respectively; U wd , U wq are the d-axis and q-axis components of the AC voltage respectively; i d , i q are the d-axis and q-axis components of the current respectively; i dref , i qref are the reference values of the d-axis and q-axis components of the current; I is the actual value of the AC current; I ref is the AC given value; ω is the angular frequency; L is the inductance value of the port reactor; t is the time;

[0020] Step S13. Based on the research in Step S12, the control parameters to be identified for the MMC-STATCOM are:

[0021] f = [K p1 , K i1 , K p2 , K i2 , K p3 , K i3 .

[0022] Preferably, the said Step S2 includes:

[0023] Step S21. Introduce a hybrid initialization strategy to improve the original whale algorithm, and use the Logistic mapping to generate a chaotic sequence to initialize the population;

[0024] Step S22. Introduce a dynamic weight strategy into the position update formula;

[0025] Step S23. In the original algorithm, the convergence factor a decreases linearly, and it cannot flexibly balance global exploration and local exploitation. By non-linearly adjusting a, the algorithm pays more attention to global search in the early stage and accelerates convergence in the later stage;

[0026] Step S24. Based on the RT-LAB simulation system, connect the RT-LAB and the actual MMC-STATCOM controller through optical fiber, test the fault response characteristics of the MMC-STATCOM under various fault conditions, and collect its simulation data set.

[0027] Preferably, in the said Step S21, the improvement formula is as follows:

[0028]

[0029] In the formula: is the initialization position of the i-th individual in the j-th dimension; Xmin,j is the lower bound of the position in the j-th dimension; X max,j is the upper bound of the position in the j-th dimension; z i,j is the i-th chaotic sequence in the j-th dimension generated by the Logistic chaotic map; η is the chaos constant, which is taken as 4 here.

[0030] Preferably, in the step S22, the improved formula after introducing the dynamic weight strategy into the position update formula is as follows:

[0031]

[0032] In the formula: w is the dynamic weight factor, t is the current iteration number, T max is the maximum iteration number, let w max = 0.9, w min = 0.2.

[0033] Preferably, in the step S23, by non-linearly adjusting a, the algorithm pays more attention to global search in the early stage and accelerates convergence in the later stage, and its improved formula is as follows:

[0034]

[0035] In the formula: γ is the attenuation rate parameter, which is taken as 1.5.

[0036] Preferably, the step S3 includes:

[0037] Step S31, initialize the parameters of the improved whale algorithm with multi-strategy fusion according to the simulation data set obtained by combining the chaotic initialization strategy in step S21 and step S24, and calculate its initial fitness;

[0038] Step S32, introduce the dynamic probability threshold p(t) to judge whether to choose to surround the prey or update by spiral;

[0039] Step S33, surround the prey or random search:

[0040] When p(t) < 0.5, calculate the whale swarm cooperation coefficients A and C:

[0041]

[0042] In the formula, r and r' are respectively two random numbers on [0,1], and a is the non-linear convergence factor in step S23;

[0043] When |A| < 1, surround the prey, and the position update formula is as follows:

[0044]

[0045] In the formula, t is the current iteration number; X *is the optimal solution position; X is the position of the search agent; D represents the distance between the whale and the prey; w is the dynamic weight factor in step S22;

[0046] When |A| ≥ 1, perform random search, and the position update formula is as follows:

[0047]

[0048] In the formula, X r represents randomly selecting the current position of a search agent; D r represents the distance between this random search agent and the prey;

[0049] Step S34, perform spiral update:

[0050] When p(t) ≥ 0.5, perform spiral update, and the position update formula is as follows:

[0051]

[0052] In the formula, b is the spiral shape constant, and l is a random number on [0, 1];

[0053] Step S35, perform boundary check on each whale and adjust the position;

[0054] Step S36, check whether the condition is satisfied. If the maximum number of iterations is reached, output the global optimal parameters;

[0055] Step S37, calculate the error. If the error is satisfied, output the optimal parameters.

[0056] Preferably, the formula for the dynamic probability threshold p(t) in step S32 is as follows:

[0057]

[0058] Here, set p max = 0.9, p min = 0.2.

[0059] Preferably, the formulas for calculating the whale swarm cooperation coefficients A and C in step S33 are as follows:

[0060]

[0061] In the formula, r and r′ are respectively random numbers on [0, 1], and a is the non-linear convergence factor in step S23.

[0062] Preferably, the error is calculated through the following formula in step S37:

[0063]

[0064] Wherein, R obs and R sim are the test value and the simulation value respectively; ε avg is the average deviation; ε abs is the average absolute deviation; ε G is the weighted average absolute deviation; ε max is the maximum deviation; N end and N fir are the serial numbers of the last data and the first data respectively.

[0065] Advantages of the present invention: The method of the present invention improves the accuracy and convergence speed of the algorithm, and realizes the accurate identification of the control parameters of MMC-STATCOM; it has good identification accuracy and strong adaptability under various working conditions, and can meet the requirements of electromagnetic transient simulation of power systems; at the same time, the high-precision MMC-STATCOM established by this identification method can better reflect its dynamic characteristics and improve the accuracy of wide-frequency oscillation risk analysis of new energy power stations. Description of the Drawings

[0066] Figure 1 is the schematic diagram of the principle of the MMC-STATCOM parameter identification method based on the improved whale algorithm with multi-strategy fusion;

[0067] Figure 2 is the schematic diagram of the MMC-STATCOM topological structure;

[0068] Figure 3 is the structure diagram of the MMC-STATCOM control model;

[0069] Figure 4 is the diagram of the MMC-STATCOM semi-physical simulation system;

[0070] Figure 5 Flow chart of the improved whale algorithm with multi-strategy fusion;

[0071] Figure 6 is the comparison diagram of the iteration curves of different algorithms;

[0072] Figure 7 is the comparison diagram of the simulation results when the low voltage ride-through Q = 0.2 p.u.;

[0073] Figure 8 is the comparison diagram of the simulation results when the low voltage ride-through Q = -0.2 p.u. Specific Embodiments

[0074] The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0075] Embodiment 1:

[0076] As Figure 1, A parameter identification method for MMC-STATCOM based on an improved whale algorithm with multi-strategy fusion, the steps include:

[0077] Step S1, analyze the control principle of MMC-STATCOM and establish its mathematical model to clarify the parameters to be identified;

[0078] Step S2, construct an improved whale algorithm with multi-strategy fusion and conduct a hardware-in-the-loop test on MMC-STATCOM to obtain hardware-in-the-loop test data;

[0079] Step S3, use the improved whale algorithm with multi-strategy fusion to identify the control parameters of MMC-STATCOM and analyze its error;

[0080] Furthermore, the specific steps of S1 are as follows;

[0081] Step S11, establish the topology of MMC-STATCOM based on the voltage sourced converter (VSC), as Figure 2 shown.

[0082] The entire MMC-STATCOM device is equivalent to a voltage source with a controllable voltage magnitude. Let the voltage generated by the MMC-STATCOM device be U C , the injected system current be I, the system voltage be U S ,, the connecting reactance be L, and the DC voltage of the converter be E. Then the current absorbed by the device is:

[0083]

[0084] The absorbed apparent power S is:

[0085]

[0086] The absorbed reactive power Q is:

[0087]

[0088] In the formula, j is the imaginary unit; is the conjugate value of the injected current; are respectively the conjugate values of the system voltage U S and the terminal voltage U of MMC-STATCOM C .

[0089] Step S12, based on the analysis of S11, the control model of MMC-STATCOM is as Figure 3 shown, and the mathematical expression is as follows:

[0090]

[0091] Where: K p1 , K i1 are the proportional and integral coefficients of the DC voltage outer loop respectively; K p2 , K i2 are the proportional and integral coefficients of the AC current outer loop respectively; K p3 , K i3 are the proportional and integral coefficients of the DC current inner loop respectively; U dc is the DC voltage value; U dcref is the DC voltage reference value; U d , U q are the d-axis and q-axis components of the voltage control signal respectively; U wd , U wq are the d-axis and q-axis components of the AC voltage respectively; i d , i q are the d-axis and q-axis components of the current respectively; i dref , i qref are the reference values of the d-axis and q-axis components of the current; I is the actual value of the AC current; I ref is the AC given value; ω is the angular frequency; L is the inductance value of the port reactor; t is the time.

[0092] Step S13, based on the research in Step S12, the control parameters to be identified for MMC-STATCOM are:

[0093] f = [K p1 , K i1 , K p2 , K i2 , K p3 , K i3

[0094] Furthermore, the specific steps of S2 are as follows;

[0095] Step S21, introduce a hybrid initialization strategy to improve the original whale algorithm. Use the Logistic map to generate a chaotic sequence to initialize the population, which can avoid the aggregation of initial solutions and improve the global search efficiency. The improved formula is as follows:

[0096]

[0097] Where: is the initialization position of the i-th individual in the j-th dimension; X min,j is the lower bound of the position in the j-th dimension; X max,j is the upper bound of the position in the j-th dimension; z i,j is the chaotic sequence of the i-th in the j-th dimension generated by the Logistic chaotic map; η is the chaotic constant, which is taken as 4 here.

[0098] ​Step S22: Introduce a dynamic weight strategy into the position update formula to adaptively adjust the step size of the whale moving towards the optimal solution, avoiding oscillations or stagnation caused by a fixed step size. The improved formula is as follows:

[0099]

[0100] In the formula: w is the dynamic weight factor, t is the current iteration number, and T max is the maximum iteration number. Let

[0101] w max = 0.9, w min = 0.2

[0102] Step S23: In the original algorithm, the convergence factor a decreases linearly, making it unable to flexibly balance global exploration and local exploitation. By non-linearly adjusting a, the algorithm can focus more on global search in the early stage and accelerate convergence in the later stage. The improved formula is as follows:

[0103]

[0104] In the formula: γ is the decay rate parameter, taking 1.5.

[0105] Step S24: As Figure 4 shown, based on the RT-LAB simulation system, connect RT-LAB to the actual MMC-STATCOM controller through optical fiber, test the fault response characteristics of MMC-STATCOM under various fault conditions, and collect its simulation data set.

[0106] Furthermore, as Figure 5 shown, step S3 specifically includes the following contents;

[0107] Step S31: Initialize the parameters of the improved whale algorithm with multi-strategy fusion according to the chaos initialization strategy in step S21 and the simulation data set obtained in step S24, and calculate its initial fitness.

[0108] Step S32: Introduce a dynamic probability threshold p(t) to determine whether to choose to surround the prey or update in a spiral:

[0109]

[0110] Here, set p max = 0.9, p min = 0.2

[0111] Step S33: Surround the prey or perform a random search:

[0112] When p(t) < 0.5, calculate the whale swarm cooperation coefficients A and C:

[0113]

[0114] In the formula, r and r' are respectively random numbers on [0, 1], and a is the non-linear convergence factor in step S23.

[0115] When |A| < 1, surround the prey, and the position update formula is as follows:

[0116]

[0117] In the formula, t is the current iteration number; X * is the optimal solution (prey) position; X is the position of the search agent (whale); D represents the distance between the whale and the prey; w is the dynamic weight factor in step S22.

[0118] When |A| ≥ 1, perform random search, and the position update formula is as follows:

[0119]

[0120] In the formula, X r represents the current position of a randomly selected search agent; D r represents the distance between this randomly searched agent and the prey.

[0121] Step S34, perform spiral update:

[0122] When p(t) ≥ 0.5, perform spiral update, and the position update formula is as follows:

[0123]

[0124] In the formula, b is the spiral shape constant (usually set to 1), and l is a random number on [0, 1]

[0125] Step S35, perform boundary check on each whale and adjust the position.

[0126] Step S36, check whether the condition is met. If the maximum iteration number is reached, output the global optimal parameters.

[0127] Step S37, calculate the error through the following formula. If the error is met, output the optimal parameters.

[0128]

[0129] In the formula, R obs and R sim are the test value and the simulation value respectively; ε avg is the mean deviation; ε abs is the mean absolute deviation; ε G is the weighted mean absolute deviation; ε max is the maximum deviation; N endand N fir are the sequence numbers of the last data and the first data respectively.

[0130] Example 2:

[0131] To verify the feasibility and accuracy of the MMC - STATCOM parameter identification method based on the improved whale algorithm with multi - strategy fusion, this paper takes the actual controller of a certain model of MMC - STATCOM from a domestic manufacturer as an example, and uses the RT - LAB platform for HIL testing to obtain its fault condition data set. The HIL test system is as Figure 5 shown, and the test results are shown in Table 1. Using the HIL data - driven improved whale algorithm, the control parameters of MMC - STATCOM are identified, then the identification results are brought into the identification model, and finally the simulation results are compared with the HIL test results to verify the effectiveness of the proposed identification method.

[0132] Table 1 Steady - state U and I before, during, and after faults under different conditions q Data / p.u.

[0133]

[0134] To verify the effectiveness and superiority of the proposed method, the GA, PSO, WOA, and IWOA algorithms are selected. Driven by the same HIL data set respectively, the corresponding iteration curves of each algorithm are obtained as shown in Figure 6 . The population size of all algorithms is 50, and the maximum number of iterations is set to 50 times.

[0135] As can be seen from Figure 6 , the PSO particle swarm algorithm is prone to falling into the global optimum, resulting in low algorithm accuracy; although the GA genetic algorithm has acceptable convergence accuracy, its convergence speed is slow; the traditional GOOSE algorithm also has the problem of slow convergence speed.

[0136] Generally speaking, compared with other intelligent algorithms, the IWOA algorithm driven by HIL data is more superior in terms of convergence speed, convergence accuracy, and global search ability, verifying the effectiveness of the proposed data - driven method.

[0137] Using the IWOA algorithm driven by HIL data to identify the parameters in the control strategy knowledge, bringing the identification results into the identification model, and comparing the response curves of the grid - connected voltage U, active current I, and active power P output by the identification model with the HIL measured results. The comparison results are shown in Figure 7 and Figure 8 . Then, the average identification error in each fault stage is calculated using the formula of S37, and the error results are shown in Table 2 and Table 3 to verify the feasibility and effectiveness of the proposed method.

[0138] Table 2 I in each fault stageq Average identification error / %

[0139]

[0140] Average identification error of U in each fault stage of Table 3 / %

[0141]

[0142] It can be seen that the deviation under each working condition is less than 5%. Therefore, it can be obtained that the proposed MMC-STATCOM parameter identification method based on the improved whale algorithm with multi-strategy fusion has good identification accuracy and strong adaptability under various working conditions, and can meet the requirements of power system electromagnetic transient simulation.

[0143] The above embodiments are only the preferred technical solutions of the present invention, and should not be regarded as a limitation to the present invention. The protection scope of the present invention should be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, the equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. An MMC-STATCOM parameter identification method based on an improved whale algorithm with multi-strategy fusion, characterized in that: It includes the following steps: Step S1, analyze the control principle of MMC-STATCOM and establish its mathematical model to clarify the parameters to be identified; Step S2, construct an improved whale algorithm with multi-strategy fusion and conduct a semi-physical test on MMC-STATCOM to obtain semi-physical test data; Step S3, use the improved whale algorithm with multi-strategy fusion to identify the control parameters of MMC-STATCOM and analyze its error.

2. The MMC-STATCOM parameter identification method based on the improved whale algorithm with multi-strategy fusion according to claim 1, characterized in that: The said Step S1 includes: Step S11, establish the topology structure of MMC-STATCOM based on the voltage source inverter; The entire MMC-STATCOM device is equivalent to a voltage source whose voltage magnitude can be controlled. Let the voltage generated by the MMC-STATCOM device be U C , the injected system current be I, and the system voltage be U S . The connecting reactance is L, and the DC voltage of the converter is E. Then the current absorbed by the device is: The absorbed apparent power S is: The absorbed reactive power Q is: where j is the imaginary unit; is the conjugate value of the injected current; are the conjugate values of the system voltage U S and the terminal voltage U C of the MMC-STATCOM, respectively; Step S12, based on the analysis of S11, the mathematical expression of the MMC-STATCOM control model is as follows: Where: K p1 , K i1 are the proportional and integral coefficients of the DC voltage outer loop respectively; K p2 , K i2 are the proportional and integral coefficients of the AC current outer loop respectively; K p3 , K i3 are the proportional and integral coefficients of the DC current inner loop respectively; U dc is the DC voltage value; U dcref is the DC voltage reference value; U d , U q are the d-axis and q-axis components of the voltage control signal respectively; U wd , U wq are the d-axis and q-axis components of the AC voltage respectively; i d , i q are the d-axis and q-axis components of the current respectively; i dref , i qref are the reference values of the d-axis and q-axis components of the current; I is the actual value of the AC current; I ref is the AC given value; ω is the angular frequency; L is the inductance value of the port reactor; t is the time; Step S13, based on the research in Step S12, the control parameters to be identified for MMC-STATCOM are: f = [K p1 , K i1 , K p2 , K i2 , K p3 , K i3 。 3. The parameter identification method of MMC-STATCOM based on the improved whale algorithm with multi-strategy fusion according to claim 2, characterized in that: The said Step S2 includes: Step S21, introduce a hybrid initialization strategy to improve the original whale algorithm, and use the Logistic mapping to generate a chaotic sequence to initialize the population; Step S22, introduce a dynamic weight strategy into the position update formula; Step S23, in the original algorithm, the convergence factor a decreases linearly and cannot flexibly balance global exploration and local exploitation. By non-linearly adjusting a, the algorithm pays more attention to global search in the early stage and accelerates convergence in the later stage; Step S24, based on the RT-LAB simulation system, connect RT-LAB and the actual MMC-STATCOM controller through optical fiber, test the fault response characteristics of MMC-STATCOM under various fault conditions, and collect its simulation data set.

4. The parameter identification method of MMC-STATCOM based on the improved whale algorithm with multi-strategy fusion according to claim 3, characterized in that: In the said Step S21, the improved formula is as follows: In the formula: is the initial position of the i-th individual in the j-th dimension; X min,j is the lower bound of the position in the j-th dimension; X max,j is the upper bound of the position in the j-th dimension; z i,j is the i-th chaotic sequence in the j-th dimension generated by the Logistic chaotic map; η is the chaotic constant, which is taken as 4 here.

5. The MMC-STATCOM parameter identification method based on the improved whale algorithm with multi-strategy fusion according to claim 3, characterized in that: In the said Step S22, the improved formula after introducing the dynamic weight strategy into the position update formula is as follows: Where: w is the dynamic weight factor, t is the current iteration number, and T max is the maximum iteration number. Let w max = 0.9, and w min = 0.

2.

6. The parameter identification method of MMC-STATCOM based on the improved whale algorithm with multi-strategy fusion according to claim 3, characterized in that: In the said Step S23, by non-linearly adjusting a to make the algorithm pay more attention to global search in the early stage and accelerate convergence in the later stage, its improved formula is as follows: In the formula: γ is the attenuation rate parameter, taking 1.

5.

7. The parameter identification method of MMC-STATCOM based on the improved whale algorithm with multi-strategy fusion according to claim 3, characterized in that: The said Step S3 includes: Step S31, initialize the parameters of the improved whale algorithm with multi-strategy fusion according to the chaotic initialization strategy in Step S21 and the simulation data set obtained in Step S24, and calculate its initial fitness; Step S32, introduce a dynamic probability threshold p(t) to judge whether to choose to surround the prey or update in a spiral; Step S33, surround the prey or perform a random search: When p(t) < 0.5, calculate the whale group cooperation coefficients A and C: In the formula, r and r′ are respectively two random numbers on [0,1], and a is the non-linear convergence factor in Step S23; When |A| < 1, surround the prey, and the position update formula is as follows: where t is the current iteration number; X * is the optimal solution position; X is the position of the search agent; D represents the distance between the whale and the prey; w is the dynamic weight factor in step S22; When |A| ≥ 1, perform a random search, and the position update formula is as follows: Wherein, X r represents randomly selecting the current position of a search agent; D r represents the distance between the random search agent and the prey; Step S34, perform a spiral update: When p(t) ≥ 0.5, perform a spiral update, and the position update formula is as follows: In the formula, b is the spiral shape constant, and l is a random number on [0,1]; Step S35, perform a boundary check on each whale and adjust the position; Step S36, check whether the condition is met. If the maximum iteration number is reached, output the global optimal parameters; Step S37, calculate the error. If the error is satisfied, output the optimal parameters.

8. The parameter identification method of MMC-STATCOM based on the improved whale algorithm with multi-strategy fusion according to claim 7, characterized in that: The formula for the dynamic probability threshold p(t) in step S32 is as follows: Set p here max = 0.9, p min = 0.2 9. The parameter identification method of MMC-STATCOM based on the improved whale algorithm with multi-strategy fusion according to claim 7, characterized in that: The formulas for calculating the whale swarm cooperation coefficients A and C in step S33 are as follows: Where r and r′ are two random numbers on [0,1], and a is the non-linear convergence factor in step S23.

10. The MMC-STATCOM parameter identification method based on the improved whale algorithm with multi-strategy fusion according to claim 7, characterized in that: In step S37, calculate the error through the following formula: where R obs and R sim are the test value and the simulation value respectively; ε avg is the mean deviation; ε abs is the mean absolute deviation; ε G is the weighted mean absolute deviation; ε max is the maximum deviation; N end and N fir are the serial numbers of the last data and the first data respectively.

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