Three-phase rectification system control method based on entropy weight multi-target particle swarm optimization

Abstract

The invention provides a three-phase rectification system control method based on entropy weight multi-target particle swarm optimization. The method comprises the steps of 1, collecting power grid historical operation data of a three-phase rectification control system and performing preprocessing; step 2, establishing a mapping relation between controller parameters and system performance indexes; 3, initializing a multi-objective particle swarm optimization algorithm, and updating individual optimal and group optimal solutions; 4, carrying out the comprehensive evaluation of the Pareto solution set through employing a TOPSIS method corrected by an entropy weight method; and step 5, applying parameters corresponding to the optimal solution set to the three-phase rectification control system. According to the method, the relation between exploration and development is balanced in the optimization process, it is ensured that not only can the parameter space be widely searched, but also the excellent solution area can be deeply excavated, and therefore the performance of the energy storage converter system is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control optimization of energy storage converters, and particularly relates to a control method for a three-phase rectification system based on entropy weight multi-objective particle swarm optimization. Background Technique

[0002] In the rapidly developing field of power electronics today, as an important bridge connecting energy and loads, the performance of the three-phase rectification control system directly affects the stability and efficiency of the entire power system. The core of the energy storage converter system lies in the design of its controller, and the optimization of controller parameters is crucial for system performance. With the continuous development of new energy technologies, such as the increasing grid connection requirements of renewable energy sources like solar energy and wind energy, the control strategy and parameter optimization problems of energy storage converters have become more complex and urgent.

[0003] Traditional controller parameter optimization methods, including classical PID control, fuzzy control, and genetic algorithms, often have certain limitations. For example, the adjustment of PID control parameters depends on the experience of engineers and is difficult to adapt to the complex and changing system dynamics; although fuzzy control has a certain degree of self-adaptability, there is a lack of a systematic optimization method for parameter adjustment.

[0004] As an approach that can get rid of the dependence on engineering experience and optimize parameter adjustment, evolutionary algorithms provide a solution for the control system optimization of energy storage converter systems. However, traditional evolutionary algorithms also have some defects: (1) Traditional evolutionary algorithms may require a large number of population individuals and multiple generations of iteration to ensure the quality and diversity of solutions, which may lead to a high computational cost.

[0005] (2) Traditional evolutionary algorithms require users to manually set and adjust multiple parameters, such as the crossover rate, mutation rate, and population size, which may be a challenge for users.

[0006] (3) When dealing with complex problems with many local optimal solutions, traditional evolutionary algorithms may prematurely converge to local optimal solutions. Summary of the Invention

[0007] Object of the Invention: The technical problem to be solved by the present invention is to provide a control method for a three-phase rectification system based on entropy weight multi-objective particle swarm optimization in view of the deficiencies of the prior art, including the following steps: Step 1, collect the historical operation data of the three-phase rectification control system and perform preprocessing. The historical operation data of the power grid includes the DC bus voltage, current, and power dynamic response time, and perform preprocessing on the data. The preprocessing includes data cleaning, normalization processing, and data order rearrangement; Step 2: Establish the mapping relationship between the controller parameters and the system performance indicators. The controller parameters include the PI parameters kp1, ki1, kp2, ki2, kp3, ki3 of the outer loop and the inner loop, and 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 (MOPSO), set the particle swarm size, the number of iterations, and the inertia weight, screen the Pareto solution set through non-dominated sorting, and update the individual optimal and the global optimal solutions. 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 balances the global search and local development capabilities by dynamically adjusting the inertia weight and the learning factor, avoids falling into the local optimal solution, and realizes voltage tracking and dynamic response optimization.

[0008] In Step 1, the preprocessing further includes: removing the abnormal data that exceeds the preset threshold range, and normalizing the data to the interval [0, 1].

[0009] In Step 2, the value range of the controller parameters is [0.0001, 50], and the calculation methods of the performance indicators are as follows: Step 2-1: In the multi-objective particle swarm optimization algorithm (MOPSO), each particle represents a candidate solution of the controller parameters (such as the PI parameters kp1, ki1, kp2, ki2, kp3, ki3 of the outer loop and the inner loop). The position and velocity of the particle are iteratively updated in the parameter space, and the Pareto optimal solution set is approximated by simulating the swarm intelligence behavior; substitute the controller parameters into the simulation model of the three-phase rectifier control system, and record the voltage dynamic response curve. Step 2-2: Calculate the performance indicators: Extract the following indicators from the response data: Settling time ST: It is defined as the time required for the voltage to rise from the initial value to within ±2% of the steady-state value. The calculation formula is: , where is the transient voltage at time t, is the steady-state value, is the steady-state holding time threshold; Overshoot OV: The percentage deviation of the maximum transient voltage from the steady-state value. The calculation formula is: , Steady-state error SSE: The average deviation of the voltage after the system stabilizes. The calculation formula is: , where len is the length of the time vector t, representing 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 representing the number of the nearest samples considered when calculating certain metrics, and is used to extract a subsequence of a fixed length from the end of the time series for calculation.

[0010] Step 3 includes: Step 3-1, initialize the particle swarm: Set parameters: the number of particles Np = 20; the maximum number of iterations ; learning factors C1 = C2 = 2 (balancing individual and group experience); inertia weight = 2; particle encoding: the position of each particle Xi = [kp1, ki1, kp2, ki2, kp3, ki3], the range of PI controller parameters is [0.0001, 50], and the velocity Vi is initialized to a random value; where kp1, ki1, kp2, ki2, kp3, ki3 represent the PI parameters of the outer and inner loops; Step 3-2, calculate the performance metrics and initialize the external archive; Calculate the metric values ST, OV, and SSE of each particle according to the method in Step 2; External archive: Initialize the Pareto solution set as an empty set, screen the non-dominated solutions in the current population through non-dominated sorting, and store them in the archive; Step 3-3, update the individual best pbest and the group best gbest; Step 3-4, update the velocity and position; Step 3-5, maintain the external archive: Non-dominated solution screening: Combine the current population and the archive, and screen the new generation of non-dominated solutions through fast non-dominated sorting; Crowding degree pruning: If the size of the archive exceeds the threshold (such as 100), use crowding degree distance sorting to retain evenly distributed solutions; Step 3-6, judge the termination condition: When reaching the maximum number of iterations terminate the algorithm.

[0011] Step 4 includes the following steps: Step 4-1, list the data of the Pareto solution set into a data matrix, and normalize the matrix to obtain the positive matrix X; Step 4-2, standardize the positive matrix: The positive matrix X is: , where n is the number of the Pareto solution set, and m is the number of performance metrics, Denote the element in the \(n\)th row and \(m\)th column of the positive matrix \(X\). The matrix obtained by normalizing the positive matrix \(X\) is denoted as \(Z\), and each element in \(Z\) is expressed as: , where denotes the element in the \(i\)th row and \(j\)th column of the matrix \(Z\); \(j\) takes values from \(1\) to \(m\); Step 4-3, calculate the score and normalize it: The matrix \(Z\) is expressed as: , Define the maximum value : denotes the maximum element in the \(m\)th column of the matrix \(Z\); Define the minimum value : , denotes the minimum element in the \(m\)th column of the matrix \(Z\); Define the distance between the \(i\)th Parato solution set and the maximum value as: , where denotes the maximum element in the \(j\)th column of the matrix \(Z\); Define the distance between the \(i\)th Parato solution set and the minimum value as: , where denotes the minimum element in the \(j\)th column of the matrix \(Z\); Obtain the unnormalized score of the \(i\)th Parato solution set : , where ; and The larger is, the smaller is, and the closer it is to the maximum value; Normalize the score , where ; denotes 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 index 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 coefficients were usually set by experience, while the present invention uses the entropy weight method to adaptively determine the weight coefficients. Through the entropy weight method, the importance of each index can be objectively reflected, avoiding the deviation caused by subjective weight assignment.

[0012] Step 4-5, modifying the TOPSIS method using the entropy weight method, including the following steps: Step 4-5-1, constructing and modifying the standardized matrix; Step 4-5-2, calculating the information entropy and weights; Step 4-6, substituting the parameter combination of the optimal solution found in Step 4-5 into the actual system for verification, and analyzing the achievement of each index.

[0013] Step 4-5-1 includes the following steps: Step 4-5-1-1, constructing the Pareto solution set into a positive matrix X, and each element in the positive matrix X represents the value of the jth performance index of the ith solution; Step 4-5-1-2, determining whether there are negative numbers in the input positive matrix X. If so, it is necessary to re-standardize it to the non-negative interval. For the matrix Z, determine whether there are negative numbers in the matrix Z. If there are, use another standardization method for the positive matrix X to obtain a non-negative matrix , and the formula of the another standardization method is: , where represents the element in the non-negative matrix at the ith row and jth column.

[0014] Step 4-5-2 includes the following steps: Step 4-5-2-1, calculating the probability matrix P based on the standardized matrix Z. The element at the ith row and jth column in P is calculated by the formula: , where ; Step 4-5-2-2, calculating the information entropy of each index, calculating the information utility value, and normalizing to obtain the entropy weight of each index. The information entropy of the jth index is calculated by the formula: ; Step 4-5-2-3, determining the information utility value , and normalizing to obtain the entropy weight of the jth index: , ; Step 4-5-3, TOPSIS comprehensive evaluation: Substitute the entropy weight into the TOPSIS method to select a set of optimal solutions from the Pareto solution set.

[0015] Step 5 includes: Outer-loop DC voltage control: By comparing the actual DC voltage and the target voltage , generate the target current by a PI regulator, which is further used as the input of the inner-loop current control; Inner-loop current control: Use the error between the direct-axis current reference value and the actual direct-axis current , and the error between the quadrature-axis current reference value and the actual quadrature-axis current as inputs, and generate the direct-axis voltage reference value and the quadrature-axis voltage reference value through independent PI controllers; Generate drive signals through coordinate transformation and SPWM modulation to dynamically adjust the switching devices.

[0016] The present invention also provides an electronic device, including a processor and a memory. The memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the above method.

[0017] The present invention also provides a storage medium storing a computer program or instruction. When the computer program or instruction runs on a computer, it executes the steps of the above method.

[0018] The present invention has the following beneficial effects: (1) Significantly improve system performance: The present invention can efficiently optimize the controller parameters of the three-phase rectifier control system through the method based on entropy-weighted multi-objective particle swarm optimization. Compared with traditional methods, the optimized system performs excellently in terms of stability, response speed, and overall efficiency.

[0019] (2) Innovative weight determination method: Introduce the TOPSIS technology modified by the entropy weight method to solve the problem of solution set selection in multi-objective optimization. This method can objectively reflect the importance of each index and avoid the deviation caused by subjective weight assignment. The weights calculated by the entropy weight method can more accurately evaluate the advantages and disadvantages of different solution sets, so as to screen out the optimal solution set. This innovative weight determination method is not only applicable to the control of the three-phase rectifier system of the present invention, but also can be extended to other multi-objective optimization problems, and has a wide application prospect.

[0020] (3) High-efficiency global search ability: Combining with the multi-objective particle swarm optimization algorithm (MOPSO), the present invention can efficiently search for the optimal solution in a complex parameter space. The MOPSO algorithm simulates the collective behavior of the particle swarm, combines the capabilities of global search and local exploitation, and avoids the problem of falling into local optimal solutions. Compared with traditional evolutionary algorithms, the MOPSO algorithm performs excellently in dealing with complex problems with many local optimal solutions, can converge to the global optimal solution faster, and greatly improves the optimization efficiency and quality.

[0021] (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 the controller parameters, and ensure that the system is always in the best operating state. This adaptive ability gives the present invention significant advantages in fields such as new energy access and industrial automation, and can effectively improve the reliability and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is the optimization flowchart of the three-phase rectification control system in the present invention.

[0023] Figure 2 is the optimization diagram of the three-phase rectification control system in the present invention.

[0024] Figure 3 is the comparison diagram of output voltage tracking under different algorithms. DETAILED IMPLEMENTATION MANNER

[0025] The following further specifically describes the present invention in conjunction with the drawings and specific implementation manners, and the above and / or other advantages of the present invention will become clearer.

[0026] As Figure 1 shown, this embodiment provides a control method for a three-phase rectification system based on entropy-weighted multi-objective particle swarm optimization, including the following steps: Step 1, collect the historical operating data of the three-phase rectification control system and perform preprocessing. The historical operating data of the power grid includes the DC bus voltage, current, and power dynamic response time, and perform preprocessing on the data. The preprocessing includes data cleaning, normalization processing, and data sequence rearrangement; Step 2, establish the mapping relationship between the controller parameters and the system performance indicators. The controller parameters include the PI parameters kp1, ki1, kp2, ki2, kp3, ki3 of the outer loop and the inner loop, and the performance indicators include the settling time ST, overshoot OV, and steady-state error SSE; Step 3: Initialize the multi-objective particle swarm optimization algorithm (MOPSO), set the particle swarm size, the number of iterations, and the inertia weight, screen the Pareto solution set through non-dominated sorting, and update the individual optimal and global optimal solutions; Step 4: Use the TOPSIS method corrected 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 balances the global search and local exploitation capabilities by dynamically adjusting the inertia weight and learning factors, avoids falling into local optimal solutions, and realizes voltage tracking and dynamic response optimization.

[0027] In Step 1, the preprocessing further includes: eliminating abnormal data beyond the preset threshold range and normalizing the data to the interval [0, 1].

[0028] In Step 2, the value range of the controller parameters is [0.0001, 50], and the calculation method of the performance index is as follows: Step 2-1: In the multi-objective particle swarm optimization algorithm (MOPSO), each particle represents a candidate solution of the controller parameters (such as the PI parameters kp1, ki1, kp2, ki2, kp3, ki3 of the outer loop and the inner loop). The position and velocity of the particle are iteratively updated in the parameter space, and the Pareto optimal solution set is approximated by simulating swarm intelligence behavior; substitute the controller parameters into the simulation model of the three-phase rectifier control system and record the voltage dynamic response curve; Step 2-2: Calculate the performance index: Extract the following indicators from the response data: Settling time ST: It is defined as the time required for the voltage to rise from the initial value to within ±2% of the steady-state value. The calculation formula is: , where is the transient voltage at time t, is the steady-state value, is the steady-state holding time threshold; Overshoot OV: The percentage deviation of the maximum transient voltage from the steady-state value. The calculation formula is: , Steady-state error SSE: The average deviation of the voltage after the system stabilizes. The calculation formula is: , where len is the length of the time vector t, representing 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, representing the number of the nearest samples considered when calculating certain metrics, and is used to extract a fixed-length subsequence from the end of the time series for calculation.

[0029] Step 3 includes: Step 3-1, initialize the particle swarm: Set parameters: the number of particles Np = 20; the maximum number of iterations ; learning factors C1 = C2 = 2 (balancing individual and population experience); inertia weight = 2; particle encoding: the position of each particle Xi = [kp1, ki1, kp2, ki2, kp3, ki3], the PI controller parameter range is [0.0001, 50], and the velocity Vi is initialized to a random value; where kp1, ki1, kp2, ki2, kp3, ki3 represent the PI parameters of the outer and inner loops; Step 3-2, calculate the performance metrics and initialize the external archive; Calculate the metric values ST, OV, and SSE of each particle according to the method in Step 2; External archive: initialize the Pareto solution set as an empty set, screen the non-dominated solutions in the current population through non-dominated sorting, and store them in the archive; Step 3-3, update the individual best pbest and the global best gbest; Step 3-4, update the velocity and position; Step 3-5, maintain the external archive: Non-dominated solution screening: merge the current population and the archive, and screen the new generation of non-dominated solutions through fast non-dominated sorting; Crowding degree pruning: if the archive size exceeds the threshold (such as 100), use crowding degree distance sorting to retain evenly distributed solutions; Step 3-6, judge the termination condition: when reaching the maximum number of iterations terminate the algorithm.

[0030] Step 4 includes the following steps: Step 4-1, list the data in the Pareto solution set as a data matrix, and normalize the matrix to obtain the positive matrix X; Step 4-2, standardize the positive matrix: the positive matrix X is: , where n is the number of Pareto solution sets, m is the number of performance metrics, Denote the element in the \(n\)th row and \(m\)th column of the positive matrix \(X\). The matrix obtained by normalizing the positive matrix \(X\) is denoted as \(Z\), and each element in \(Z\) is expressed as: , where denotes the element in the \(i\)th row and \(j\)th column of the matrix \(Z\); \(j\) takes values from 1 to \(m\); Step 4-3, calculate the score and normalize it: The matrix \(Z\) is expressed as: , Define the maximum value : denotes the maximum element in the \(m\)th column of the matrix \(Z\); Define the minimum value : , denotes the minimum element in the \(m\)th column of the matrix \(Z\); Define the distance between the \(i\)th Parato solution set and the maximum value as: , where denotes the maximum element in the \(j\)th column of the matrix \(Z\); Define the distance between the \(i\)th Parato solution set and the minimum value as: , where denotes the minimum element in the \(j\)th column of the matrix \(Z\); Obtain the unnormalized score of the \(i\)th Parato solution set : , where ; and the larger is, the smaller is, and the closer it is to the maximum value; Normalize the score , where ; denotes 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 index 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 coefficients were usually set by experience, while the present invention uses the entropy weight method to adaptively determine the weight coefficients. Through the entropy weight method, the importance of each index can be objectively reflected, avoiding the deviation caused by subjective weight assignment.

[0031] Step 4-5, using the entropy weight method to modify the TOPSIS method, including the following steps: Step 4-5-1, constructing and modifying the standardized matrix; Step 4-5-2, calculating the information entropy and weights; Step 4-6, substituting the parameter combination of the optimal solution found in Step 4-5 into the actual system for verification, and analyzing the achievement of each index.

[0032] Step 4-5-1 includes the following steps: Step 4-5-1-1, constructing the Pareto solution set into a positive matrix X, and each element in the positive matrix X represents the j-th performance index value of the i-th solution; Step 4-5-1-2, determining whether there are negative numbers in the input positive matrix X. If so, it is necessary to re-normalize to the non-negative interval. For the matrix Z, determine whether there are negative numbers in the matrix Z. If there are, use another normalization method for the positive matrix X to obtain a non-negative matrix , and the formula for the another normalization method is: , where represents the element in the i-th row and j-th column of the non-negative matrix .

[0033] Step 4-5-2 includes the following steps: Step 4-5-2-1, based on the standardized matrix Z, calculating the probability matrix P, and the element in the i-th row and j-th column of P is calculated by the formula: , where ; Step 4-5-2-2, calculating the information entropy of each index, calculating the information utility value, and normalizing to obtain the entropy weight of each index. The information entropy of the j-th index is calculated by the formula: ; Step 4-5-2-3, determining the information utility value , and normalizing to obtain the entropy weight of the j-th index: , ; Step 4-5-3, TOPSIS comprehensive evaluation: Substitute the entropy weight into the TOPSIS method to select a set of optimal solutions from the Pareto solution set.

[0034] Step 5 includes: Outer-loop DC voltage control: By comparing the actual DC voltage and the target voltage , generate the target current by a PI regulator, which is further used as the input of the inner-loop current control; Inner-loop current control: Use the error between the direct-axis current reference value and the actual direct-axis current , and the error between the quadrature-axis current reference value and the actual quadrature-axis current as inputs, and generate the direct-axis voltage reference value and the quadrature-axis voltage reference value through independent PI controllers; Generate a drive signal through coordinate transformation and SPWM modulation to dynamically adjust the switching devices.

[0035] In another specific embodiment of the present invention, a control method for a three-phase rectification system based on entropy-weight multi-objective particle swarm optimization is provided, including the following steps: Step 1, collect historical power grid data, including various combinations of fitting the energy storage converter controller parameters , , , , , and the corresponding performance index time (ST), overshoot (OV), steady-state error (SSE), where , , , , are randomly selected, but the approximate ranges of these values are [0.0001, 50].

[0036] Step 2, preprocess the data. The preprocessing operations include data cleaning, removing unreasonable data, randomly shuffling and rearranging the data order, and normalizing the data, etc.

[0037] Step 3, design the MOPSO algorithm indicators. The parameters include fitting the energy storage converter controller parameters , , , , , Various combinations and the corresponding performance index time (ST), overshoot (OV), and steady-state error (SSE).

[0038] Step 4, design the constraint function: , where is the weight coefficient and also the entropy weight obtained by TOPSIS corrected based on the entropy weight method, = 0.5914, = 0.2298, = 0.1788.

[0039] Step 5, substitute the corresponding entropy weight into TOPSIS to obtain the optimal solution as = 36, , = 13.34172.

[0040] Step 6, substitute the optimal parameter combination obtained in Step 5 into the system, compare it with the other two methods, and draw a comparison graph of each performance index under different methods, as Figure 3 shown.

[0041] As Figure 2 shown, this embodiment provides a control method for a three-phase rectifier system based on entropy-weighted multi-objective particle swarm optimization. The core control structure of the system includes: Outer-loop DC voltage control: By comparing the actual DC voltage and the target voltage , the target current is generated by the PI regulator and further used as the input of the inner-loop current control.

[0042] Inner-loop current control: Using the error between the target current and (where ) and the actual current and as the input, the target voltages and are generated by independent PI (proportional-integral) controllers.

[0043] Coordinate transformation module: Park transformation and Clarke transformation: Convert the three-phase current into DC components and , thus achieving more efficient control.

[0044] Inverse transformation module: Convert the target voltages and into three-phase target voltages , , .

[0045] 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.

[0046] Intelligent optimization algorithm module: Using MOPSO algorithm, dynamically adjust PI controller parameters to further improve system performance and adapt to complex operating environments.

[0047] like Figure 3 As shown in FIG. 1 , this embodiment provides a comparison diagram of output voltage tracking under different algorithms. As can be seen from the figure, the MOPSO algorithm can approach the steady-state output value at a faster speed in the initial stage. This fast response capability is due to the adaptive weight mechanism of the MOPSO algorithm, which can more efficiently explore and utilize the search space to obtain a better solution.

[0048] In the steady-state stage, the output voltage curve of the MOPSO algorithm is almost completely consistent with the target value (green dashed line). This shows that the MOPSO algorithm has higher accuracy in tracking the target output and can achieve optimization of small errors, showing its good dynamic response ability. Compared with the Particle Swarm Optimization (PSO) algorithm, this moderate overshoot can quickly reach the target value while avoiding excessive fluctuations.

[0049] This embodiment introduces a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the three-phase rectifier system control method based on entropy weight multi-objective particle swarm optimization is implemented.

[0050] This embodiment introduces a computer device, including: a memory for storing instructions.

[0051] The processor is used to execute the instructions so that the computer device performs the operation of the three-phase rectifier system control method based on entropy weight multi-objective particle swarm optimization.

[0052] 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1means for the functions specified in one or more boxes.

[0053] These computer program instructions can 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 a manufactured article including an instruction device, and the instruction device implements the processes Figure 1 one or more processes and / or boxes Figure 1 the functions specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more processes and / or boxes Figure 1 one or more boxes.

[0055] The present invention provides a control method for a three-phase rectification system based on entropy-weighted multi-objective particle swarm optimization. There are many methods and ways to specifically implement this technical solution. The above description is only a preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in this embodiment can be implemented by existing technologies.

Claims

1. A control method for a three-phase rectifier system based on entropy weight multi-objective particle swarm optimization, characterized in that, It includes the following steps: Step 1: Collect the historical operation data of the three-phase rectifier control system and preprocess it. The historical operation data of the power grid includes the DC bus voltage, current, and power dynamic response time, and preprocess the data. The preprocessing includes data cleaning, normalization processing, and data sequence rearrangement; Step 2: Establish the mapping relationship between the controller parameters and the system performance indicators. The controller parameters include the PI parameters kp1, ki1, kp2, ki2, kp3, ki3 of the outer loop and the inner loop, and the performance indicators include the settling time ST, overshoot OV, and steady-state error SSE; Step 3: Initialize the multi-objective particle swarm optimization algorithm, set the particle swarm size, the number of iterations, and the inertia weight, screen the Pareto solution set through non-dominated sorting, and update the individual optimal and population optimal solutions; Step 4: Use the TOPSIS method corrected 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 realizes voltage tracking and dynamic response optimization by dynamically adjusting the inertia weight and learning factor.

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

3. The method according to claim 2, wherein In Step 2, the value range of the controller parameters is [0.0001, 50], and the calculation methods of the performance indicators are as follows: Step 2-1: In the multi-objective particle swarm optimization algorithm, each particle represents a candidate solution of the controller parameters. The position and velocity of the particle are iteratively updated in the parameter space, and the Pareto optimal solution set is approximated by simulating swarm intelligence behavior; substitute the controller parameters into the simulation model of the three-phase rectifier control system and record the voltage dynamic response curve; Step 2-2: Calculate the performance indicators: Extract the following indicators from the response data: The calculation formula for the settling time ST is: ， wherein is the transient voltage at time t, is the steady-state value, is the steady-state holding time threshold; The calculation formula for the overshoot OV is: ， The calculation formula for the steady-state error SSE is: ， where len is the length of the time vector t; Ns is an input parameter.

4. The method according to claim 3, wherein Step 3 includes: Step 3-1: Initialize the particle swarm: Set parameters, including: the number of particles, the maximum number of iterations, learning factors C1, C2, and the inertia weight ; Particle coding: The position of each particle Xi = [kp1, ki1, kp2, ki2, kp3, ki3], the range of the PI controller parameters is [0.0001, 50], and the velocity Vi is initialized to a random value; Step 3-2: Calculate the performance indicators and initialize the external archive; Calculate the index values ST, OV, SSE of each particle according to the method in Step 2; External archive: Initialize the Pareto solution set as an empty set, screen the non-dominated solutions in the current population through non-dominated sorting, and store them in the archive; Step 3-3: Update the individual optimal pbest and the population optimal gbest; Step 3-4: Update the velocity and position; Step 3-5: Maintain the external archive: Non-dominated solution screening: Combine the current population and the archive, and screen the new generation of non-dominated solutions through fast non-dominated sorting; Crowding degree pruning: If the archive size exceeds the threshold, use the crowding degree distance sorting to retain the uniformly distributed solutions; Step 3-6, termination condition judgment: terminate the algorithm when the maximum number of iterations is reached at this time.

5. The method according to claim 4, characterized in that, Step 4 includes the following steps: Step 4-1: List the data in the Pareto solution set as a data matrix and normalize the matrix to obtain the positive matrix X. Step 4-2: Standardize the positive matrix: The positive matrix X is: ， where n is the number of Pareto solution sets and m is the number of performance indicators. represents the element in the n-th row and m-th column of the positive matrix X. The matrix obtained by normalizing the positive matrix X is denoted as Z, and each element in Z is expressed as: ， wherein represents the element in the i-th row and j-th column of matrix Z; j ranges from 1 to m; Step 4-3: Calculate the scores and normalize them: The matrix Z is expressed as: ， Define the maximum value : represents the maximum element in the m-th column of matrix Z; Define the minimum value : ， Denote the minimum element of the m-th column of matrix Z; Define the distance between the $i$-th Pareto solution set and the maximum value as follows: ， wherein represents the maximum element of the j-th column of matrix Z; Define the distance between the $i$-th Pareto solution set and the minimum value as follows: ， wherein represents the minimum element of the j-th column of matrix Z; Derive the unnormalized score of the i-th Pareto solution set : ， Among them ; Score Normalize: where ; 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 index parameter, , , and are weight coefficients, 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; Step 4-5: Modify the TOPSIS method using the entropy weight method, including the following steps: Step 4-5-1: Construct and modify the standardized matrix; Step 4-5-2: Calculate the information entropy and weights; Step 4-6: Substitute the parameter combination of the optimal solution found in Step 4-5 into the actual system for verification and analyze the achievement of each index.

6. The method according to claim 5, wherein Step 4-5-1 includes the following steps: Step 4-5-1-1, construct the Pareto solution set into a positive matrix X, and each element in the positive matrix X represents the j-th 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 there are, it is necessary to re-normalize it to the non-negative interval. For matrix Z, determine whether there are negative numbers in matrix Z. If there are, use another normalization method for the positive matrix X to obtain a non-negative matrix , and the formula for the another normalization method is: ， wherein represents a non-negative matrix and is the element at the i-th row and j-th column in 7. The method according to claim 6, characterized in that, Step 4-5-2 includes the following steps: Step 4-5-2-1, calculate the probability matrix P based on the standardized matrix Z. The element in the i-th row and j-th column of P is calculated according to the following formula: ， Among them ; Step 4-5-2-2: Calculate the information entropy of each indicator, calculate the information utility value, and normalize it to obtain the entropy weight of each indicator. The information entropy of the j-th indicator is calculated by the following formula: ； Step 4-5-2-3, determine the information utility value , and normalize it to obtain the entropy weight of the j-th index : ， ； Step 4-5-3: TOPSIS comprehensive evaluation: Substitute the entropy weight into the TOPSIS method and select a set of optimal solutions from the Pareto solution set.

8. The method according to claim 7, wherein Step 5 includes: Outer-loop DC voltage control: By comparing the actual DC voltage and the target voltage , the target current is generated by a PI regulator and further used as the input for the inner-loop current control; Inner-loop current control: Using the direct-axis current reference value and the actual direct-axis current error, the quadrature-axis current reference value and the actual quadrature-axis current error as inputs, generate the direct-axis voltage reference value and the quadrature-axis voltage reference value through an independent PI controller; Generate a driving signal through coordinate transformation and SPWM modulation to dynamically adjust the switching device.

9. An electronic device, characterized in that, It includes a processor and a memory, and the memory stores program code. When the program code is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 8.

10. A storage medium, characterized in that, Stores a computer program or instruction. When the computer program or instruction runs on a computer, it executes the steps of the method according to any one of claims 1 to 8.

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