A control method for three-phase rectifier system based on entropy-weighted multi-objective particle swarm optimization

Abstract

The present invention provides a three-phase rectifier system control method based on entropy-weighted multi-objective particle swarm optimization, comprising: step 1, collecting and preprocessing historical grid operation data of the three-phase rectifier control system; step 2, establishing a mapping relationship between controller parameters and system performance indicators; step 3, initializing the multi-objective particle swarm optimization algorithm and updating the individual optimal and group optimal solutions; step 4, using the TOPSIS method modified by the entropy weight method to comprehensively evaluate the Pareto solution set; and step 5, applying the parameters corresponding to the optimal solution set to the three-phase rectifier control system. This method balances the relationship between exploration and exploitation during the optimization process, ensuring both a broad search of the parameter space and a deep exploration of the excellent solution region, thereby significantly improving the performance of the energy storage converter system.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control optimization of energy storage converters, and in particular relates to a three-phase rectifier system control method based on entropy weighted multi-objective particle swarm optimization. Background Art

[0002] In today's rapidly developing field of power electronics, three-phase rectifier control systems serve as a crucial bridge between energy sources and loads, and their performance directly impacts the stability and efficiency of the entire power system. The core of energy storage converter systems lies in their controller design, and optimizing controller parameters is crucial to system performance. With the continuous development of new energy technologies, such as the growing demand for grid-connected renewable energy sources like solar and wind power, the control strategies and parameter optimization issues of energy storage converters have become increasingly complex and pressing.

[0003] Traditional controller parameter optimization methods, including classic PID control, fuzzy control, and genetic algorithms, often have limitations. For example, PID control parameter adjustment relies on the engineer's experience and is difficult to adapt to complex and changing system dynamics. While fuzzy control has a certain degree of adaptability, it lacks a systematic optimization method for parameter adjustment.

[0004] As a method that can break away from reliance on engineering experience and optimize parameter adjustment, evolutionary algorithms provide a solution for optimizing the control system of energy storage converter systems. However, traditional evolutionary algorithms also have some drawbacks:

[0005] (1) Traditional evolutionary algorithms may require a large number of individuals and multiple generations of iterations to ensure the quality and diversity of solutions, which may result in high computational costs.

[0006] (2) Traditional evolutionary algorithms require users to manually set and adjust multiple parameters, such as crossover rate, mutation rate, and population size, which may be a challenge for users.

[0007] (3) When traditional evolutionary algorithms deal with complex problems with many local optimal solutions, they may converge to the local optimal solution too early. Summary of the Invention

[0008] Purpose of the invention: The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and provide a three-phase rectifier system control method based on entropy-weighted multi-objective particle swarm optimization, comprising the following steps:

[0009] Step 1: Collect and pre-process historical grid operation data of the three-phase rectifier control system, wherein the historical grid operation data includes DC bus voltage, current, and power dynamic response time, and pre-process the data, wherein the pre-processing includes data cleaning, normalization, and data reordering;

[0010] Step 2: Establish a mapping relationship between controller parameters and system performance indicators. The controller parameters include the PI parameters kp1, ki1, kp2, ki2, kp3, and ki3 of the outer and inner loops. The performance indicators include the settling time ST, the overshoot OV, and the steady-state error SSE.

[0011] Step 3: Initialize the multi-objective particle swarm optimization algorithm (MOPSO), set the particle swarm size, number of iterations, and inertia weight, filter the Pareto solution set through non-dominated sorting, and update the individual optimal solution and the group optimal solution.

[0012] Step 4: Use the TOPSIS method modified by the entropy weight method to comprehensively evaluate the Pareto solution set;

[0013] In step 5, the parameters corresponding to the optimal solution set are applied to the three-phase rectifier control system. The multi-objective particle swarm optimization algorithm balances global search and local development capabilities by dynamically adjusting the inertia weight and learning factor, avoids falling into the local optimal solution, and realizes voltage tracking and dynamic response optimization.

[0014] In step 1, the preprocessing further includes: removing abnormal data that exceeds a preset threshold range, and normalizing the data to the interval [0, 1].

[0015] In step 2, the value range of the controller parameter is [0.0001, 50], and the calculation method of the performance index is:

[0016] In step 2-1, in the multi-objective particle swarm optimization algorithm (MOPSO), each particle represents a candidate solution for the controller parameters (such as the PI parameters kp1, ki1, kp2, ki2, kp3, and ki3 for the outer and inner loops). The particle's position and velocity are iteratively updated in the parameter space, simulating swarm intelligence behavior to approximate the Pareto optimal solution set. The controller parameters are substituted into the simulation model of the three-phase rectifier control system, and the voltage dynamic response curve is recorded.

[0017] Step 2-2, calculate performance indicators:

[0018] The following metrics are extracted from the response data:

[0019] Settling time ST: It is defined as the time required for the voltage to rise from the initial value to the steady-state value within ±2%. The calculation formula is:

[0020] ,

[0021] in is the transient voltage at time t, is the steady-state value, is the steady-state holding time threshold;

[0022] Overshoot OV: The percentage deviation between the maximum transient voltage and the steady-state value. The calculation formula is:

[0023] ,

[0024] Steady-state error SSE: The average deviation of the voltage after the system stabilizes. The calculation formula is:

[0025] ,

[0026] Where len is the length of the time vector t, indicating the total number of data points, which is used to control the loop range and as a boundary condition for certain calculations; Ns is an input parameter indicating the number of recent samples considered when calculating certain indicators, used to extract a fixed-length subsequence from the end of the time series for calculation.

[0027] Step 3 includes:

[0028] Step 3-1, initialize the particle swarm:

[0029] Setting parameters: number of particles Np=20; maximum number of iterations ; Learning factor C1=C2=2 (balance individual and group experience); Inertia weight =2; particle encoding: each particle position Xi = [kp1, ki1, kp2, ki2, kp3, ki3], PI controller parameter range is [0.0001, 50], speed Vi is initialized to a random value; where kp1, ki1, kp2, ki2, kp3, ki3 represent the PI parameters of the outer loop and inner loop;

[0030] Step 3-2, calculate performance indicators and initialize external archive;

[0031] Calculate the index values ​​ST, OV, and SSE of each particle according to the method in step 2;

[0032] External archiving: Initialize the Pareto solution set to an empty set, filter the non-dominated solutions in the current population through non-dominated sorting, and save them in the archive;

[0033] Step 3-3, update the individual optimal pbest and group optimal gbest;

[0034] Step 3-4, speed and position update;

[0035] Steps 3-5, maintain external archives:

[0036] Non-dominated solution screening: merge the current population with the archive, and screen the next generation of non-dominated solutions through Fast Non-Dominated Sorting;

[0037] Crowding pruning: If the archive size exceeds a threshold (e.g., 100), crowding distance sorting is used to retain evenly distributed solutions.

[0038] Step 3-6, termination condition judgment: when the maximum number of iterations is reached The algorithm is terminated when .

[0039] Step 4 includes the following steps:

[0040] Step 4-1, list the data of the Pareto solution set into a data matrix, and perform the matrix forward transformation to obtain the forward matrix X;

[0041] Step 4-2, standardize the forward matrix: the forward matrix X is:

[0042] ,

[0043] Where n is the number of Pareto solution sets, m is the number of performance indicators, Represents the element in the nth row and mth column of the forward matrix X. The matrix of the normalized forward matrix X is recorded as Z. Each element in Z is expressed as:

[0044] ,

[0045] in Represents the element in the i-th row and j-th column of the matrix Z; j ranges from 1 to m;

[0046] Step 4-3, calculate the score and normalize it:

[0047] The matrix Z is represented as:

[0048] ,

[0049] Define the maximum value : Represents the maximum element of the mth column of matrix Z;

[0050] Define the minimum value :

[0051] ,

[0052] Represents the minimum element of the mth column of matrix Z;

[0053] Define the distance between the i-th Parato solution set and the maximum value for:

[0054] ,

[0055] in Represents the maximum element of the j-th column of matrix Z;

[0056] Define the distance between the i-th Parato solution set and the minimum value for:

[0057] ,

[0058] in represents the minimum element of the j-th column of matrix Z;

[0059] Get the unnormalized score of the i-th Parato solution set :

[0060] ,

[0061] in ;and The bigger, The smaller it is, the closer it is to the maximum value;

[0062] will score Normalization: ,in ; represents the normalized score;

[0063] Step 4-4, construct the constraint function;

[0064] The constraint function F is defined as:

[0065] ,

[0066] where f i is the i-th performance indicator parameter, 、 、 and is the weight coefficient, f1 is the tracking speed index of the output voltage; f2 is the static error of the output voltage; f3 is the overshoot of the output voltage; the previous weight coefficient is usually set by experience, while the present invention adopts the entropy weight method to adapt the weight coefficient. Through the entropy weight method, the importance of each indicator can be objectively reflected, avoiding the deviation caused by subjective weighting.

[0067] Step 4-5, using the entropy weight method to modify the TOPSIS method, includes the following steps:

[0068] Step 4-5-1, construction and correction of standardization matrix;

[0069] Step 4-5-2, calculate information entropy and weight;

[0070] Step 4-6: Substitute the optimal solution parameter combination found in step 4-5 into the actual system for verification and analyze the achievement of each indicator.

[0071] Step 4-5-1 includes the following steps:

[0072] Step 4-5-1-1, construct the Pareto solution set as a forward matrix X, and forward each element in the matrix X represents the jth performance index value of the i-th solution;

[0073] Step 4-5-1-2, determine whether there are negative numbers in the input positive matrix X, if so, re-normalize it to the non-negative interval, for the matrix Z, determine whether there are negative numbers in the matrix Z, if so, use another normalization method on the positive matrix X to obtain a non-negative matrix , the formula of the other standardization method is:

[0074] ,

[0075] in represents a non-negative matrix The element at row i and column j in .

[0076] Step 4-5-2 includes the following steps:

[0077] Step 4-5-2-1, based on the normalized matrix Z, calculate the probability matrix P, the element in the i-th row and j-th column of P The calculation formula is:

[0078] ,

[0079] in ;

[0080] Step 4-5-2-2, calculate the information entropy of each indicator, calculate the information utility value, and normalize it to get the entropy weight of each indicator. The information entropy of the jth indicator is The calculation formula is:

[0081] ;

[0082] Step 4-5-2-3, determine the information utility value , and normalize to get the entropy weight of the jth indicator :

[0083] ,

[0084] ;

[0085] Step 4-5-3, TOPSIS comprehensive evaluation: Bring the entropy weight into the TOPSIS method and select a set of optimal solutions from the Pareto solution set.

[0086] Step 5 includes:

[0087] Outer loop DC voltage control: by comparing the actual DC voltage and target voltage , the target current is generated by the PI regulator , further serves as the input of the inner loop current control;

[0088] Inner loop current control: using direct axis current reference value and the actual direct-axis current Error, quadrature-axis current reference value and the actual quadrature-axis current The error is used as input to generate the direct axis voltage reference value through an independent PI controller and quadrature-axis voltage reference ;

[0089] The drive signal is generated through coordinate transformation and SPWM modulation to dynamically adjust the switching device.

[0090] The present invention also provides an electronic device, comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the method.

[0091] The present invention also provides a storage medium storing a computer program or instruction, which executes the steps of the method when the computer program or instruction is run on a computer.

[0092] The present invention has the following beneficial effects: (1) Significantly improves system performance: The present invention can efficiently optimize the controller parameters of a three-phase rectifier control system by using an entropy-weighted multi-objective particle swarm optimization method. Compared with traditional methods, the optimized system performs well in terms of stability, response speed, and overall efficiency.

[0093] (2) Innovative weight determination method: The TOPSIS technique modified by the entropy weight method is introduced to solve the problem of solution set selection in multi-objective optimization. This method can objectively reflect the importance of each indicator and avoid the deviation caused by subjective weighting. The weights calculated by the entropy weight method can more accurately evaluate the advantages and disadvantages of different solution sets, thereby screening out the optimal solution set. This innovative weight determination method is not only applicable to the three-phase rectifier system control of the present invention, but can also be extended to other multi-objective optimization problems, and has broad application prospects.

[0094] (3) Efficient global search capability: Combined with the multi-objective particle swarm optimization algorithm (MOPSO), the present invention can efficiently search for optimal solutions in complex parameter spaces. The MOPSO algorithm avoids the problem of being trapped in local optimal solutions by simulating the collective behavior of particle swarms and combining the capabilities of global search and local development. Compared with traditional evolutionary algorithms, the MOPSO algorithm performs well in dealing with complex problems with many local optimal solutions and can converge to the global optimal solution more quickly, greatly improving optimization efficiency and quality.

[0095] (4) Adaptability to complex operating environments: The control method of the present invention has good adaptability and can adapt to complex power grid operating environments and changing load demands. Through the dynamic adjustment of the intelligent optimization algorithm module, the system can respond to power grid fluctuations and load changes in real time, automatically optimize controller parameters, and ensure that the system is always in the best operating state. This adaptive capability gives the present invention significant advantages in the fields of new energy access and industrial automation, and can effectively improve the reliability and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 This is the optimization flow chart of the three-phase rectifier control system in the present invention.

[0097] Figure 2 It is the optimization diagram of the three-phase rectifier control system in the present invention.

[0098] Figure 3 This is a comparison chart of output voltage tracking under different algorithms. DETAILED DESCRIPTION

[0099] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more apparent.

[0100] like Figure 1 As shown, this embodiment provides a three-phase rectifier system control method based on entropy weighted multi-objective particle swarm optimization, comprising the following steps:

[0101] Step 1: Collect and pre-process historical grid operation data of the three-phase rectifier control system, wherein the historical grid operation data includes DC bus voltage, current, and power dynamic response time, and pre-process the data, wherein the pre-processing includes data cleaning, normalization, and data reordering;

[0102] Step 2: Establish a mapping relationship between controller parameters and system performance indicators. The controller parameters include the PI parameters kp1, ki1, kp2, ki2, kp3, and ki3 of the outer and inner loops. The performance indicators include the settling time ST, the overshoot OV, and the steady-state error SSE.

[0103] Step 3: Initialize the multi-objective particle swarm optimization algorithm (MOPSO), set the particle swarm size, number of iterations, and inertia weight, filter the Pareto solution set through non-dominated sorting, and update the individual optimal solution and the group optimal solution.

[0104] Step 4: Use the TOPSIS method modified by the entropy weight method to comprehensively evaluate the Pareto solution set;

[0105] In step 5, the parameters corresponding to the optimal solution set are applied to the three-phase rectifier control system. The multi-objective particle swarm optimization algorithm balances global search and local development capabilities by dynamically adjusting the inertia weight and learning factor, avoids falling into the local optimal solution, and realizes voltage tracking and dynamic response optimization.

[0106] In step 1, the preprocessing further includes: removing abnormal data that exceeds a preset threshold range, and normalizing the data to the interval [0, 1].

[0107] In step 2, the value range of the controller parameter is [0.0001, 50], and the calculation method of the performance index is:

[0108] In step 2-1, in the multi-objective particle swarm optimization algorithm (MOPSO), each particle represents a candidate solution for the controller parameters (such as the PI parameters kp1, ki1, kp2, ki2, kp3, and ki3 for the outer and inner loops). The particle's position and velocity are iteratively updated in the parameter space, simulating swarm intelligence behavior to approximate the Pareto optimal solution set. The controller parameters are substituted into the simulation model of the three-phase rectifier control system, and the voltage dynamic response curve is recorded.

[0109] Step 2-2, calculate performance indicators:

[0110] The following metrics are extracted from the response data:

[0111] Settling time ST: It is defined as the time required for the voltage to rise from the initial value to the steady-state value within ±2%. The calculation formula is:

[0112] ,

[0113] in is the transient voltage at time t, is the steady-state value, is the steady-state holding time threshold;

[0114] Overshoot OV: The percentage deviation between the maximum transient voltage and the steady-state value. The calculation formula is:

[0115] ,

[0116] Steady-state error SSE: The average deviation of the voltage after the system stabilizes. The calculation formula is:

[0117] ,

[0118] Where len is the length of the time vector t, indicating the total number of data points, which is used to control the loop range and as a boundary condition for certain calculations; Ns is an input parameter indicating the number of recent samples considered when calculating certain indicators, used to extract a fixed-length subsequence from the end of the time series for calculation.

[0119] Step 3 includes:

[0120] Step 3-1, initialize the particle swarm:

[0121] Setting parameters: number of particles Np=20; maximum number of iterations ; Learning factor C1=C2=2 (balance individual and group experience); Inertia weight =2; particle encoding: each particle position Xi = [kp1, ki1, kp2, ki2, kp3, ki3], PI controller parameter range is [0.0001, 50], speed Vi is initialized to a random value; where kp1, ki1, kp2, ki2, kp3, ki3 represent the PI parameters of the outer loop and inner loop;

[0122] Step 3-2, calculate performance indicators and initialize external archive;

[0123] Calculate the index values ​​ST, OV, and SSE of each particle according to the method in step 2;

[0124] External archiving: Initialize the Pareto solution set to an empty set, filter the non-dominated solutions in the current population through non-dominated sorting, and save them in the archive;

[0125] Step 3-3, update the individual optimal pbest and group optimal gbest;

[0126] Step 3-4, speed and position update;

[0127] Steps 3-5, maintain external archives:

[0128] Non-dominated solution screening: merge the current population with the archive, and screen the next generation of non-dominated solutions through Fast Non-Dominated Sorting;

[0129] Crowding pruning: If the archive size exceeds a threshold (e.g., 100), crowding distance sorting is used to retain evenly distributed solutions.

[0130] Step 3-6, termination condition judgment: when the maximum number of iterations is reached The algorithm is terminated when .

[0131] Step 4 includes the following steps:

[0132] Step 4-1, list the data of the Pareto solution set into a data matrix, and perform the matrix forward transformation to obtain the forward matrix X;

[0133] Step 4-2, standardize the forward matrix: the forward matrix X is:

[0134] ,

[0135] Where n is the number of Pareto solution sets, m is the number of performance indicators, Represents the element in the nth row and mth column of the forward matrix X. The matrix of the normalized forward matrix X is recorded as Z. Each element in Z is expressed as:

[0136] ,

[0137] in Represents the element in the i-th row and j-th column of the matrix Z; j ranges from 1 to m;

[0138] Step 4-3, calculate the score and normalize it:

[0139] The matrix Z is represented as:

[0140] ,

[0141] Define the maximum value : Represents the maximum element of the mth column of matrix Z;

[0142] Define the minimum value :

[0143] ,

[0144] Represents the minimum element of the mth column of matrix Z;

[0145] Define the distance between the i-th Parato solution set and the maximum value for:

[0146] ,

[0147] in Represents the maximum element of the j-th column of matrix Z;

[0148] Define the distance between the i-th Parato solution set and the minimum value for:

[0149] ,

[0150] in represents the minimum element of the j-th column of matrix Z;

[0151] Get the unnormalized score of the i-th Parato solution set :

[0152] ,

[0153] in ;and The bigger, The smaller it is, the closer it is to the maximum value;

[0154] will score Normalization: ,in ; represents the normalized score;

[0155] Step 4-4, construct the constraint function;

[0156] The constraint function F is defined as:

[0157] ,

[0158] where f i is the i-th performance indicator parameter, 、 、 and is the weight coefficient, f1 is the tracking speed index of the output voltage; f2 is the static error of the output voltage; f3 is the overshoot of the output voltage; the previous weight coefficient is usually set by experience, while the present invention adopts the entropy weight method to adapt the weight coefficient. Through the entropy weight method, the importance of each indicator can be objectively reflected, avoiding the deviation caused by subjective weighting.

[0159] Step 4-5, using the entropy weight method to modify the TOPSIS method, includes the following steps:

[0160] Step 4-5-1, construction and correction of standardization matrix;

[0161] Step 4-5-2, calculate information entropy and weight;

[0162] Step 4-6: Substitute the optimal solution parameter combination found in step 4-5 into the actual system for verification and analyze the achievement of each indicator.

[0163] Step 4-5-1 includes the following steps:

[0164] Step 4-5-1-1, construct the Pareto solution set as a forward matrix X, and forward each element in the matrix X represents the jth performance index value of the i-th solution;

[0165] Step 4-5-1-2, determine whether there are negative numbers in the input positive matrix X, if so, re-normalize it to the non-negative interval, for the matrix Z, determine whether there are negative numbers in the matrix Z, if so, use another normalization method on the positive matrix X to obtain a non-negative matrix , the formula of the other standardization method is:

[0166] ,

[0167] in represents a non-negative matrix The element at row i and column j in .

[0168] Step 4-5-2 includes the following steps:

[0169] Step 4-5-2-1, based on the normalized matrix Z, calculate the probability matrix P, the element in the i-th row and j-th column of P The calculation formula is:

[0170] ,

[0171] in ;

[0172] Step 4-5-2-2, calculate the information entropy of each indicator, calculate the information utility value, and normalize it to get the entropy weight of each indicator. The information entropy of the jth indicator is The calculation formula is:

[0173] ;

[0174] Step 4-5-2-3, determine the information utility value , and normalize to get the entropy weight of the jth indicator :

[0175] ,

[0176] ;

[0177] Step 4-5-3, TOPSIS comprehensive evaluation: Bring the entropy weight into the TOPSIS method and select a set of optimal solutions from the Pareto solution set.

[0178] Step 5 includes:

[0179] Outer loop DC voltage control: by comparing the actual DC voltage and target voltage , the target current is generated by the PI regulator , further serves as the input of the inner loop current control;

[0180] Inner loop current control: using direct axis current reference value and the actual direct-axis current Error, quadrature-axis current reference value and the actual quadrature-axis current The error is used as input to generate the direct axis voltage reference value through an independent PI controller and quadrature-axis voltage reference ;

[0181] The drive signal is generated through coordinate transformation and SPWM modulation to dynamically adjust the switching device.

[0182] In another specific embodiment of the present invention, a three-phase rectifier system control method based on entropy-weighted multi-objective particle swarm optimization is provided, comprising the following steps:

[0183] Step 1: Collect historical grid data, including fitting the energy storage converter controller parameters 、 、 、 、 Various combinations of and corresponding performance indicators time (ST), overshoot (OV), steady-state error (SSE), where 、 、 、 、 The selection of is random, but the values ​​are roughly in the range [0.0001, 50].

[0184] Step 2: Preprocess the data. Preprocessing operations include data cleaning, eliminating unreasonable data, reordering the data, and normalizing the data.

[0185] Step 3: Design the MOPSO algorithm index, including the parameters of the energy storage converter controller. 、 、 、 、 、 Various combinations of and corresponding performance indicators time (ST), overshoot (OV), steady-state error (SSE).

[0186] Step 4: Design constraint function:

[0187] ,

[0188] in is the weight coefficient, which is also the entropy weight obtained by TOPSIS based on the entropy weight method. =0.5914, =0.2298, =0.1788.

[0189] Step 5: Bring the corresponding entropy weight into TOPSIS to obtain the optimal solution: =36, , =13.34172.

[0190] Step 6: Bring the optimal parameter combination obtained in step 5 into the system and compare it with the other two methods. Draw a comparison chart of each performance indicator under different methods, such as Figure 3 shown.

[0191] like Figure 2 As shown, this embodiment provides a three-phase rectifier system control method based on entropy-weighted multi-objective particle swarm optimization. The core control structure of the system includes:

[0192] Outer loop DC voltage control: by comparing the actual DC voltage and target voltage , the target current is generated by the PI regulator , further serves as the input of the inner loop current control.

[0193] Inner loop current control: using target current and (in ) and actual current and The error is used as input to generate the target voltage through an independent PI (proportional-integral) controller and .

[0194] Coordinate transformation module: Park transformation and Clarke transformation: convert three-phase current into DC component and , thus achieving more efficient control.

[0195] Inverse conversion module: convert the target voltage and Convert to three-phase target voltage 、 、 .

[0196] SPWM modulation module: Generates three-phase PWM signals through space vector pulse width modulation (SPWM) to drive the switching devices of the energy storage converter.

[0197] Intelligent optimization algorithm module: uses the MOPSO algorithm to dynamically adjust PI controller parameters to further improve system performance and adapt to complex operating environments.

[0198] like Figure 3 As shown in Figure 1, this embodiment provides a comparison of output voltage tracking using different algorithms. As can be seen from the figure, the MOPSO algorithm is able to approach the steady-state output value relatively quickly in the initial stage. This rapid response capability is due to the MOPSO algorithm's adaptive weighting mechanism, which enables more efficient exploration and utilization of the search space, resulting in a more optimal solution.

[0199] During the steady-state phase, the MOPSO algorithm's output voltage curve is almost identical to the target value (dashed green line). This demonstrates that the MOPSO algorithm tracks the target output with greater precision, enabling optimization with minimal errors and demonstrating excellent dynamic response. Compared to the particle swarm optimization (PSO) algorithm, this moderate overshoot allows for faster target attainment while avoiding excessive fluctuations.

[0200] This embodiment introduces a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the three-phase rectifier system control method based on entropy-weighted multi-objective particle swarm optimization is implemented.

[0201] This embodiment introduces a computer device, including: a memory for storing instructions.

[0202] The processor is used to execute the instructions so that the computer device performs the operations of the three-phase rectifier system control method based on entropy weight multi-objective particle swarm optimization.

[0203] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0204] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0205] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0206] The present invention provides a three-phase rectifier system control method based on entropy-weighted multi-objective particle swarm optimization. There are many methods and approaches to implement this technical solution. The above is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention. Such improvements and modifications should also be considered within the scope of protection of the present invention. Any components not specified in this embodiment may be implemented using existing technologies.

Claims

1. A three-phase rectifier system control method based on entropy weighted multi-objective particle swarm optimization, characterized in that: The following steps are involved: Step 1: Collect and pre-process historical grid operation data of the three-phase rectifier control system, wherein the historical grid operation data includes DC bus voltage, current, and power dynamic response time, and pre-process the data, wherein the pre-processing includes data cleaning, normalization, and data reordering; Step 2: Establish a mapping relationship between controller parameters and system performance indicators. The controller parameters include the PI parameters kp1, ki1, kp2, ki2, kp3, and ki3 of the outer and inner loops. The performance indicators include the settling time ST, the overshoot OV, and the steady-state error SSE. Step 3: Initialize the multi-objective particle swarm optimization algorithm, set the particle swarm size, number of iterations, and inertia weight, filter the Pareto solution set through non-dominated sorting, and update the individual optimal solution and the group optimal solution. Step 4: Use the TOPSIS method modified by the entropy weight method to comprehensively evaluate the Pareto solution set; Step 5: Apply the parameters corresponding to the optimal solution set to the three-phase rectifier control system. The multi-objective particle swarm optimization algorithm dynamically adjusts the inertia weight and learning factor to achieve voltage tracking and dynamic response optimization. In step 2, the value range of the controller parameter is [0.0001, 50], and the calculation method of the performance index is: In step 2-1, in the multi-objective particle swarm optimization algorithm, each particle represents a candidate solution for the controller parameters. The particle's position and velocity are iteratively updated in the parameter space, simulating swarm intelligence behavior to approximate the Pareto optimal solution set. The controller parameters are substituted into the simulation model of the three-phase rectifier control system, and the voltage dynamic response curve is recorded. Step 2-2, calculate performance indicators: The following metrics are extracted from the response data: The formula for calculating the setup time ST is: ， in is the transient voltage at time t, is the steady-state value, is the steady-state holding time threshold; The calculation formula for overshoot OV is: ， The calculation formula of steady-state error SSE is: ， where len is the length of the time vector t; N s is an input parameter; Step 3 includes: Step 3-1, initialize the particle swarm: Set parameters, including: number of particles, maximum number of iterations, learning factors C1, C2, and inertia weight ; Particle encoding: Each particle position Xi = [kp1, ki1, kp2, ki2, kp3, ki3], PI controller parameter range is [0.0001, 50], velocity Vi is initialized to a random value; Step 3-2, calculate performance indicators and initialize external archive; Calculate the index values ​​ST, OV, and SSE of each particle according to the method in step 2; External archiving: Initialize the Pareto solution set to an empty set, filter the non-dominated solutions in the current population through non-dominated sorting, and save them in the archive; Step 3-3, update the individual optimal pbest and the group optimal gbest; Step 3-4, speed and position update; Steps 3-5, maintain external archives: Non-dominated solution screening: merge the current population with the archive, and screen the next generation of non-dominated solutions through fast non-dominated sorting; Crowding pruning: If the archive size exceeds a threshold, crowding distance sorting is used to retain evenly distributed solutions; Step 3-6, termination condition judgment: when the maximum number of iterations is reached The algorithm is terminated when Step 5 includes: Outer loop DC voltage control: by comparing the actual DC voltage and target voltage , the target current is generated by the PI regulator , further serves as the input of the inner loop current control; Inner loop current control: using direct axis current reference value and the actual direct-axis current Error, quadrature-axis current reference value and the actual quadrature-axis current The error is used as input to generate the direct axis voltage reference value through an independent PI controller and quadrature-axis voltage reference ; The drive signal is generated through coordinate transformation and SPWM modulation to dynamically adjust the switching device.

2. The method according to claim 1, characterized in that In step 1, the preprocessing further includes: removing abnormal data that exceeds a preset threshold range, and normalizing the data to the interval [0, 1].

3. The method according to claim 2, characterized in that Step 4 includes the following steps: Step 4-1, list the data of the Pareto solution set into a data matrix, and perform the matrix forward transformation to obtain the forward matrix X; Step 4-2, standardize the forward matrix: the forward matrix X is: ， Where n is the number of Pareto solution sets, m is the number of performance indicators, Represents the element in the nth row and mth column of the forward matrix X. The matrix of the normalized forward matrix X is recorded as Z. Each element in Z is expressed as: ， in Represents the element in the i-th row and j-th column of the matrix Z; j ranges from 1 to m; Step 4-3, calculate the score and normalize it: The matrix Z is represented as: ， Define the maximum value : Represents the maximum element of the mth column of matrix Z; Define the minimum value : ， Represents the minimum element of the mth column of matrix Z; Define the distance between the i-th Parato solution set and the maximum value for: ， in Represents the maximum element of the j-th column of matrix Z; Define the distance between the i-th Parato solution set and the minimum value for: ， in represents the minimum element of the j-th column of matrix Z; Get the unnormalized score of the i-th Parato solution set : ， in ; will score Normalization: ,in ; represents the normalized score; Step 4-4, construct the constraint function; The constraint function F is defined as: ， where f i is the i-th performance indicator parameter, 、 、 and is the weight coefficient, f1 is the tracking speed index of the output voltage; f2 is the static error of the output voltage; f3 is the output voltage overshoot; Step 4-5, using the entropy weight method to modify the TOPSIS method, includes the following steps: Step 4-5-1, construction and correction of standardization matrix; Step 4-5-2, calculate information entropy and weight; Step 4-6: Substitute the optimal solution parameter combination found in step 4-5 into the actual system for verification and analyze the achievement of each indicator.

4. The method according to claim 3, characterized in that Step 4-5-1 includes the following steps: Step 4-5-1-1, construct the Pareto solution set as a positive matrix X, and positively transform each element in the matrix X represents the jth performance index value of the i-th solution; Step 4-5-1-2, determine whether there are negative numbers in the input positive matrix X, if so, re-normalize it to the non-negative interval, for the matrix Z, determine whether there are negative numbers in the matrix Z, if so, use another normalization method on the positive matrix X to obtain a non-negative matrix , the formula of the other standardization method is: ， in represents a non-negative matrix The element at row i and column j in .

5. The method according to claim 4, characterized in that Step 4-5-2 includes the following steps: Step 4-5-2-1, based on the normalized matrix Z, calculate the probability matrix P, the element in the i-th row and j-th column of P The calculation formula is: ， in ; Step 4-5-2-2, calculate the information entropy of each indicator, calculate the information utility value, and normalize it to get the entropy weight of each indicator. The information entropy of the jth indicator is The calculation formula is: ； Step 4-5-2-3, determine the information utility value , and normalize to get the entropy weight of the jth indicator : ， ； Step 4-5-3, TOPSIS comprehensive evaluation: Bring the entropy weight into the TOPSIS method and select a set of optimal solutions from the Pareto solution set.

6. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 5.

7. A storage medium, characterized in that: A computer program or instruction is stored, and when the computer program or instruction is run on a computer, the steps of the method according to any one of claims 1 to 5 are executed.

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