Control method of three-phase rectification system based on multi-objective hybrid elite particle swarm optimization

Abstract

The invention provides a control method of a three-phase rectification system based on multi-objective mixed elite particle swarm optimization. The control method comprises the following steps: step 1, selecting an objective function; step 2, performing minimization optimization on the target function; step 3, obtaining a circuit value through simulation operation of the circuit; step 4, calculating the dominating relation of the target values of all particles, and finding out all non-dominated solutions; 5, using the particles with the minimum entropy and the particles with the high quality to form mixed elite particles to represent global optimal particles; 6, dynamically adjusting the flight coefficient of the mixed elite particles; step 7, updating the speed and the position of the particle; 8, repeating the steps 3-7 until a set condition is reached; step 9, when the number of archived particles exceeds the maximum capacity, deleting the archived particles; and step 10, outputting the particles in the final archiving. According to the method, target independence is kept, weight distribution is avoided, and target conflicts are better processed.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control optimization of three-phase rectification, and in particular relates to a control method of a three-phase rectification system based on multi-objective hybrid elite particle swarm optimization. Background Art

[0002] In electronic power systems, three-phase rectifier systems, as important equipment in the field of power electronics, have the advantages of low grid-side current harmonic content, high power factor, bidirectional energy flow, and fast dynamic response. With the continuous development of power electronics technology, the control performance requirements for three-phase rectifiers are becoming higher and higher. Research on high-performance three-phase rectifier control strategies has important practical significance. In recent years, in order to achieve efficient and fast DC output, many studies have chosen space vector pulse width modulation (SVPWM) as an advanced control strategy in the modulation technology of three-phase rectifier circuits. In SVPWM control, it mainly relies on experience to adjust three groups of PI (proportional-integral) controllers. The setting of these controller parameters often requires the debugger to adjust them based on personal experience and understanding of the system. There are several main problems with this method:

[0003] Subjectivity: The debugging process is highly dependent on the personal experience and expertise of the debugger, which may lead to different control results obtained by different debuggers under the same conditions. This dependence limits the repeatability and versatility of the control strategy, making it difficult to maintain consistency in different scenarios or between different debuggers.

[0004] Difficulty of optimization: Parameter adjustment of PI controller usually relies on trial and error, gradually approaching the optimal solution through multiple tests. This process is not only time-consuming, but also inefficient, and it is difficult to ensure that the true global optimal solution is found. In addition, due to the complexity of parameter adjustment, even after multiple adjustments, it may not be possible to achieve the desired control effect.

[0005] Different environments require different performance requirements: Traditional optimization algorithms, when optimizing in combination with specific performance, only obtain a set of solutions based on the fitness function, and this set of solutions may not be able to adapt to different environmental requirements, such as situations with better requirements for ST or OV, SSE, etc.

[0006] In order to solve these problems, researchers began to explore the use of algorithms to optimize PI control parameters. Among them, evolutionary algorithms have been widely studied due to their advantages in solving complex optimization problems. Evolutionary algorithms can find approximate optimal solutions in multi-objective optimization problems by simulating natural selection and genetic mechanisms. However, there are also some challenges when applying evolutionary algorithms to three-phase rectifier control optimization:

[0007] Weight relationship of multi-objective performance parameters: If the overall system needs to consider multiple performances during the design process, then when designing the fitness function, it is necessary to use weight coefficients to linearly combine different objectives. In this process, the setting of weight coefficient values ​​may cause the optimization results to be biased towards certain specific objectives, while ignoring other equally important objectives.

[0008] The adaptability of a single solution is poor: Using ordinary particle swarm optimization or other genetic algorithms, what is finally obtained is a set of solutions based on the fitness function. The solution is found based on the fitness function, and as the external environment changes, the solution may not be better adapted to different performance requirements. For example, it cannot adapt to external conditions with higher requirements for OV performance.

[0009] The effect of traditional multi-objective particle swarm optimization is not good: In the traditional multi-objective particle swarm optimization algorithm, because it is different from the ordinary particle swarm algorithm that can directly calculate the global optimal particle, if bad particles are selected during the update process, it may not be possible to find a better Pareto solution set. Summary of the invention

[0010] Purpose of the invention: The technical problem to be solved by the present invention is to provide a control method for a three-phase rectifier system based on multi-objective hybrid elite particle swarm optimization in view of the shortcomings of the prior art.

[0011] The present invention focuses on key technologies such as multi-objective optimization, electronic power, SVPWM and three-phase rectification. By establishing a three-phase rectification model based on SVPWM modulation, a more efficient output of the three-phase rectification circuit is achieved. Through the analysis of the actual circuit performance, a suitable performance indicator is selected as the target, and an improved multi-objective particle swarm algorithm is used. Then, through the analysis of the archived particles, the selection of hybrid elite particles and the adjustment of adaptive flight parameters are completed to achieve the optimal algorithm performance. Finally, the optimal Pareto solution set is obtained through the optimization of the algorithm, and the objective function values ​​of different performances in the Pareto solution set can be used to select the PI controller parameter solution for the most suitable environment. The method of the present invention specifically includes the following steps: Step 1, selecting the performance indicators of the three-phase rectifier circuit as the objective function of the multi-objective particle swarm algorithm, the performance indicators include the settling time ST (Settling Time, ST, the time required for the system response to reach and remain within a certain range of the final steady-state value), overvoltage OV (Over Voltage, OV, the difference between the maximum voltage and the reference voltage) and steady state error SSE (Steady State Error, SSE, the voltage error when the system reaches a stable state); Step 2, using a multi-objective particle swarm algorithm to minimize and optimize the objective function; Step 3, bringing the value of the particle obtained by initialization into the PI controller, and obtaining the required circuit value by simulating the circuit, wherein the value of the particle obtained by initialization refers to three groups of proportional-integral parameters pi (Proportional-Integral) and proportional-derivative parameters pd (Proportional-Derivative), expressed as (kp1, ki1, kp2, ki2, kp3, ki3); wherein kp1, ki1 represent the proportional-integral parameters Pi and the proportional-derivative parameters pd of the first group of PI controllers respectively; kp2, ki2 represent the proportional-integral parameters Pi and the proportional-derivative parameters pd of the second group of PI controllers respectively; kp3, ki3 represent the proportional-integral parameters Pi and the proportional-derivative parameters pd of the third group of PI controllers respectively; Then, three target values ​​represented by each particle are calculated respectively through three sets of target functions, wherein the three target values ​​include the stabilization time ST, the overshoot OV and the stabilization error SSE; Step 4, calculate the mutual dominance relationship of the target values ​​of all particles, find all non-dominated solutions, and store all non-dominated solutions in the archive; Step 5: Analyze the archived particles, use the entropy weight method to calculate the minimum entropy particles, use the roulette method to select particles with higher quality (better fitness), use the minimum entropy particles and the higher quality particles to represent the global optimal particles in the speed update process of the multi-objective particle algorithm, and use the minimum entropy particles and the higher quality particles to form a mixed elite particle to represent the global optimal particle; Step 6, when using the hybrid elite particles obtained in the above process, because the influence of the flight coefficient needs to be considered in the calculation process, the dominance method is used to calculate the dominance ratio of the particle with the minimum entropy in the entire archived particles, and normalize the ratio to achieve dynamic adjustment of the flight coefficient of the hybrid elite particles; Step 7, update the particle speed and position; Step 8, repeat steps 3 to 7 until the set condition is reached (such as reaching the maximum number of update generations); Step 9: During the update process, when the number of archived particles exceeds the set maximum capacity, the particles in the archive are deleted; Step 10, output the particles in the final archive. At this time, the solution set composed of all particles in the archive is the final Pareto solution set (Pareto solution set is a concept in multi-objective optimization problems, which refers to the set of solutions that cannot be further optimized between all objective functions. Specifically, if a solution is better than other solutions in at least one objective and is not inferior to other solutions in other objectives, then the solution is called a Pareto optimal solution. The set of all Pareto optimal solutions is the Pareto solution set).

[0012] In step 1, space vector pulse width modulation (SVPWM) is used to obtain the parameters of the three-phase rectifier circuit, including kp1, ki1, kp2, ki2, kp3, and ki3.

[0013] In step 2, ST (Settling Time): is the system response to reach and maintain the output voltage reference value The time required for the voltage value after three-phase rectification to be within the allowable error range. The allowable error range is defined by the threshold PCTvst, and the upper and lower bounds ub and lb of the error range are: (1), (2), The objective functions of ST, OV, and SSE are: ST is the time when the system response vol(t) (the final output voltage changes with time) first enters [lb,ub] and remains in this range. The objective function is: (3), Where vol(t) is the final output voltage as a function of time t; It means that for any time greater than time t , all satisfied ; OV (Over Voltage): is the system response exceeding the reference value V ref The maximum deviation of the objective function is: (4), SSE (Sum of Squared Errors): is the difference between the system response in the steady state phase (the last Ns time points) and the reference value V ref The sum of square errors, the objective function is: (5), Where len is the length of time t and Ns is the number of time points in the steady-state phase.

[0014] In this invention experiment, V ref Set to 750V, PCTvst to 0.02, and Ns to 500.

[0015] Step 4 includes: setting a multi-objective optimization problem, the objective function is , where m is the number of objectives, for two solutions x_1 and x_2, if for all objective functions , i takes values ​​from 1 to m, the objective function of the solution x_1 is at least as good as the solution x_2, that is For all All are true, and at least one of the goals Make , then it is determined that solution x_1 dominates solution x_2, denoted as x_1 , expressed as: (6).

[0016] In step 5, the archived particles are analyzed and the entropy weight method is used to calculate the particle with the minimum entropy, which specifically includes: Step 5-11, initialization of multi-objective particle swarm algorithm: Initialize and design the parameters of the multi-objective particle swarm algorithm, including the population size, the setting of the first generation of particles, the maximum iteration number, archiving, the capacity of the archived particles, the flight coefficient and the mutation rate; Step 5-12, use the entropy weight method to find elite particles: among the archived particles, use the entropy weight method to calculate the particle with the smallest information entropy, which specifically includes the following steps: Step 5-12-1, data input: The solution set consisting of archived particles is taken as the original matrix; the original matrix is ​​an n×3 matrix, 3 represents three dimensions—OV, ST, SSE, and n represents the number of particles in the archive; Step 5-12-2, data normalization: (7), in is the normalized element of the original matrix in row i and column j. is the i-th row and j-th column element of the original matrix, , are the minimum and maximum values ​​of the j-th column of the original matrix respectively; Step 5-12-3, calculate the probability distribution matrix: (8), in is the i-th row and j-th column element of the probability distribution matrix, is the sum of all elements of the normalized matrix; Step 5-12-4, calculate information entropy: (9), in is the information entropy of the jth target value (the target value represents three specific targets, namely ST, OV, and SSE); Step 5-12-5, calculate the information entropy weight: (10), in, is the information entropy weight of the jth objective function; Step 5-12-6, calculate the normalized weight: (11), in is the normalized weight, is the sum of all information entropy weights; Step 5-12-7, calculate the weighted value: (12), in, is the weighted value of the i-th particle; Step 5-12-8, get the index of the minimum weighted value: L (13), Where L is The position index of the minimum value is the index of the minimum position of the weighted information entropy.

[0017] The index of the particle with the minimum information entropy is obtained by calculation. The particle with the minimum information entropy means that the uncertainty of the particle in the target space is lower, the amount of information provided is larger, and it can better reflect the distribution of the objective function, which helps to maintain the diversity of the population. In addition, the particle with the minimum information entropy can provide more useful information, help the algorithm better explore new solution space, and improve the global search capability.

[0018] In step 5, the roulette wheel selection is based on the fitness value of each particle to calculate the selection probability and make the selection by cumulative probability; The roulette wheel method is used to select particles with higher quality, specifically comprising: Step 5-21, calculate the selection probability: For each particle i, the selection probability for: (14), in is the fitness value of the ith particle, and N is the total number of archived particles; Step 5-22, calculate the cumulative probability: Cumulative probability P i For implementing roulette selection: (15), in represents the selection probability of the e-th particle, P i represents the cumulative probability of the i-th particle; Step 5-23, select particles: Generate a random number r∈[0,1] and select the smallest particle i that satisfies the following conditions: (16), In the present invention, the roulette method is based on hypercube selection and includes the following steps: Step 5-24, select the hypercube: REP.quality is a matrix, where each row represents a hypercube and the quality of the hypercube. The second column of the matrix REP.quality REP.quality(:,2) contains the quality value of each hypercube; Step 5-25, calculate the selection probability of each hypercube h : (17), Step 5-26, Select Hypercube: Use roulette wheel selection mechanism, based on The value of selects a hypercube: Generate a random number r between the set [0,1] and calculate: (18), Where R represents a random number r that generates a A random number within the maximum range; Find the first hypervolume h_1 that satisfies the roulette condition: R≤ (19), in represents the cumulative probability of the hypervolume h_1 meeting the roulette condition; Step 5-27, randomly select a particle H in the hypervolume h_1 as a particle with a higher mass selected by the roulette method; The calculation method of the hypercube and REP.quality in this step: For all particles in the archived particles, in the solution space, the solution space is a three-dimensional space because of the three objective functions in this technical design. In this three-dimensional space, the entire solution space is divided and numbered using the set grid, where each grid is a hypercube, REP.quality() matrix, where REP means archive, the first column represents the hypercube number with particles, and the second column is the quality calculation formula, which is 10 divided by the number of particles in each hypercube with particles. For example: there are two hypercubes, numbered 3,5, and the number of particles is 1,5, then: REP.quality() = (20).

[0019] Roulette selection tends to favor solutions with better quality, thus guiding the search to explore known high-quality areas. Since roulette selection has a certain degree of randomness, it will increase the exploration of particles, thereby helping the algorithm explore new solution spaces. Roulette selection is based on the ratio of quality values. Particles with higher quality values ​​have a greater probability of being selected. This mechanism can dynamically adjust the selection pressure to adapt to the search needs at different stages. For example, in the early stages of the search, the solution quality varies greatly, and roulette selection can be more inclined to high-quality particles; in the later stages of the search, the solution quality varies less, and roulette selection can select particles more evenly.

[0020] Step 6 includes: Step 6-1, calculate the maximum and minimum values ​​of each objective function value in the archive: For each objective value j, calculate the maximum and minimum values ​​of all solutions in the archive: , (twenty one), in represents the target value of the i-th particle on the j-th objective function, , are the maximum and minimum values ​​of the jth objective function respectively; Step 6-2, calculate dominance: For each solution in the archive (The particles in the archive are solutions. In multi-objective optimization, each generated particle is a solution.) Calculate the dominance of the particle X with the minimum information entropy on each objective function: (twenty two), in It represents the value of the jth objective function of the particle X with the minimum information entropy. is the dominance of particle X with the minimum information entropy over particle i on the j-th objective function, represents the target value of particle i on the objective function j, represents the maximum value of the objective function j, Represents the minimum value of the objective function j; If the particle X with the smallest information entropy is better than particle i in the jth objective function, the dominance degree on the jth objective function is accumulated: when (twenty three), Where M is the number of objective functions (valued at 3), is the total dominance of particle X with the smallest information entropy over the i-th particle; Step 6-3, calculate the total dominance: Calculate the total dominance G of the particle X with the smallest information entropy over all solutions in the archive: (twenty four), Step 6-4, find the minimum value in the dominance: find the minimum value of the dominance of the particle X with the minimum information entropy over all solutions in the archive : (25), Step 6-5: Calculate the ratio of minimum dominance to total dominance : (26), Step 6-6, calculate the adaptive flight coefficient: , (27), in is the flight coefficient of the roulette high-mass particle, is the flight coefficient of the particle with the minimum information entropy, and C2 is the basic flight coefficient of the global optimal particle.

[0021] Here, by introducing the dominance degree and adaptively adjusting the flight coefficients in front of the two elite particles, the global search capability of the algorithm can be better adjusted during the search process, and the exploration of the positions of the two elite particles can be better balanced. In addition, by the dominance degree of other particles in the archive, the relative position of the particles in the target space can be better evaluated. The smaller the dominance degree, the relatively isolated particle in the target space, and the larger the range of target values ​​around it, which can ensure the uniformity of the solution distribution and prevent the algorithm from converging to the local optimal solution too early.

[0022] Step 7 includes: The particle velocity update formula is: (28), The particle position update formula is: (29), in represents the velocity of particle i at the next moment, represents the velocity of particle i at the current moment, is the weight ratio of particle i in the current speed update process, C1 is the flight coefficient of the individual optimal position, is the individual optimal position of particle i, represents the current position of particle i, represents the position of particle i at the next moment, is the particle with the smallest information entropy, For Roulette high quality particles, , , A random number between 0 and 1.

[0023] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0024] The present invention also provides a computer device, comprising: a memory for storing instructions; a processor for executing the instructions, so that the computer device executes the method described.

[0025] Beneficial effect: When optimizing the PI controller, the traditional particle swarm algorithm is abandoned, because in the traditional particle swarm algorithm, if multiple performance indicators of the circuit need to be considered, then when designing the fitness function of the particle swarm algorithm, multiple performance parameters need to be linearly grouped together through weight coefficients. In this way, how to select the weight coefficient is crucial. When designing the weight coefficient, the performance of the algorithm will be greatly affected due to subjectivity. However, in the present invention, multi-objective optimization is used, the fitness function is abandoned, and the performance indicator is directly used as the objective function. Then, they are selected from each other through the dominance relationship, the independence of the objectives is maintained, the weight distribution is avoided, and the conflict of objectives is better handled.

[0026] In the process of updating the particle speed, a hybrid elite particle of two particles calculated based on the entropy weight method and the roulette method is used to represent the global optimal particle for updating. The particle with the smallest information entropy obtained by the entropy weight method has a smaller information entropy, which means that the uncertainty of the particle in the target space is lower, the amount of information provided is larger, and it can better reflect the distribution of the objective function, which helps to maintain the diversity of the population. The roulette high-quality particle indicates that the particle is in a known high-quality area in the solution space, thereby guiding the search to the known high-quality area and promoting the algorithm to find a more accurate Pareto solution set.

[0027] When using hybrid elite particles, because their respective flight coefficients need to be considered, the dominance calculation method is sampled in this experiment to adaptively adjust the flight coefficients, which can make the algorithm more adaptable to environmental changes and provide its anti-interference ability, so as to better improve the quality of the solution set.

[0028] The multi-objective optimization algorithm will eventually output a Pareto solution set through an archived design and the use of dominance, so that the overall experiment, i.e. the three-phase rectifier circuit, can autonomously select appropriate PI controller parameters according to the requirements of the external environment to adapt to the requirements of different external environments. For example, if the external environment has higher requirements on the performance of time (ST), the solution with the best ST relative to other Pareto solutions can be selected. Similarly, it can be used in external environments with higher requirements for OV or SSE. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1It is a flow chart of the multi-objective particle swarm algorithm based on hybrid elite particles of the present invention.

[0030] Figure 2 It is a control system optimization diagram of the three-phase rectifier circuit in the embodiment.

[0031] Figure 3 It is a comparison of the Pareto solution set space scatter plots before and after improvement as well as their mutual dominance relationship.

[0032] Figure 4 It is a graph showing the output voltage of particle 1, particle 2, and particle 3 in the archived particles changing with time.

[0033] Figure 5 It is a graph showing the output voltage of particles 4, 5, and 6 in the archived particles changing with time.

[0034] Figure 6 It is a graph showing the output voltage of particles 7, 8, and 9 in the archived particles changing with time.

[0035] Figure 7 It is a graph showing the output voltage of particles 10, 11 and 12 in the archived particles changing with time.

[0036] Figure 8 It is a graph showing the output voltage of particles 13, 14 and 15 in the archived particles changing with time.

[0037] Fig. 9 It is a graph showing the output voltage of particles 16, 17 and 18 in the archived particles changing with time.

[0038] Fig.10 It is a curve graph showing the output voltage of particles 19, 20 and 21 in the archived particles changing with time.

[0039] Fig.11 It is a graph showing the output voltage of particles 22, 23 and 24 in the archived particles changing with time.

[0040] Fig.12 It is a curve graph showing the output voltage of particles 25, 26 and 27 in the archived particles changing with time.

[0041] Fig.13 It is a comparison of the improved Pareto solution set and the solution scatter diagram obtained by the PSO (Particle Swarm Optimization) algorithm, as well as the dominance relationship.

[0042] Fig.14 It is the relationship diagram between output voltage and time obtained by particle 24 and PSO algorithm in the improved Pareto solution set. DETAILED DESCRIPTION

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0044] This embodiment introduces a multi-objective optimization three-phase rectifier system based on hybrid elite particles, including the following steps: Step 1, determine the objective function of the multi-objective particle swarm optimization algorithm, refer to the performance indicators of the three-phase rectifier circuit, select three performance indicators ST (time), OV (overshoot), SSE (steady-state error) as the objective function. In the selection process, if too many objective functions are selected (more than three), the optimization problem will be introduced into high-dimensional multi-objective optimization, which will greatly reduce the performance of the algorithm. If there are fewer objective functions, the final result cannot well represent the overall performance of the circuit. Therefore, in this experiment, the above three performance indicators are selected as the objective function.

[0045] Step 2, such as Figure 1 Shown is a flow chart of a multi-objective particle swarm algorithm based on hybrid elite particles. First, the multi-objective particle swarm algorithm is initialized.

[0046] Step 2.1, initialize the initial values ​​of the particles, that is, the control parameters of the three groups of PI controllers ki1, kp1, ki2, kp2, ki3, and kp3.

[0047] Step 2.2, set the population size, the value is set to 20 in this invention. Set the archive particle capacity, which is set to 30 in this invention. Set the basic flight coefficient, individual flight coefficient C1=2, hybrid elite particle basic flight coefficient C2=2, current speed weight maximum value , minimum .

[0048] Step 2.3, set the maximum iteration number Gmax = 1000. Particle boundary var = [0.0001, 50].

[0049] Step 2.4, set the mutation probability. In the present invention, the best mutation probability is obtained after multiple experiments. .

[0050] Step 3, assign values ​​to the first generation particles, use random numbers to randomly assign values ​​to particles within the set boundary range, then interact with the simulation, substitute the parameters of all particles into the PI controller of the simulation circuit, extract the required parameter variables, namely the simulation time and the output data of the load voltage, and then substitute the relevant variables into the three objective functions, and calculate the three objective function values ​​respectively. Because it is the first generation of particles, the particle position and objective function value are directly assigned to the optimal position Pbest of the current particle history and the optimal target value Pbest_fit of the particle history. Then confirm the dominance relationship between all particles. The dominance between particles is defined as follows: Suppose there is a multi-objective optimization problem, whose objective function is , where m is the number of objectives, for two solutions x and y, if for all objective functions , the objective function of the solution x is at least as good as the solution y, that is For all All are true, and at least one of the goals , so that , then we say that solution x dominates solution y, denoted by , which is expressed mathematically as: (1) By determining the domination relationship between all particles, all non-dominated particles are stored in the archive, so they are the first generation of particles, so there is no need to consider the capacity of the archive.

[0051] Step 4: Update the speed and position of particles in the multi-objective particle swarm algorithm.

[0052] Step 4.1, analyze the particles in the archived particles, refer to the definition of information entropy calculation in the above content, calculate and select the particles with the smallest information entropy.

[0053] Step 4.2, analyze the particles in the archived particles. Refer to the definition of roulette calculation in the above content to calculate and select high-quality particles.

[0054] Step 4.3: Analyze the particles in the archived particles. Refer to the definition of dominance calculation in the above content to calculate the dominance of the particle with the minimum information entropy over the entire archived particles. And calculate the flight coefficient of each hybrid elite particle.

[0055] Step 4.4, based on the current generation and the maximum generation and the current speed weight ratio , minimum , the current speed weight ratio in the adaptive update process, formula: (2), Where ger represents the number of generations currently updated, and Gmax represents the maximum number of iterations, which is set to 1000 in the present invention. The design feature of this method is that in the early stage of the algorithm, by increasing the speed weight ratio, the global search capability can be enhanced, so that particles can explore the solution space more widely and avoid premature convergence to the local optimal solution. In the later stage of the algorithm, by reducing the speed weight ratio, the local search capability can be enhanced, so that particles can search the local area more carefully and improve the accuracy of the solution.

[0056] Step 4.5, based on the variables obtained in steps 4.1 to 4.5 above, update the velocity and position of the current particle with reference to the following formula: Speed ​​update formula: (3), Position update formula: (4), Step 5, interact with the simulation, set all PI controller parameters of the mutated particles into the PI controller, obtain simulation data, namely simulation time and output voltage, by running the simulation circuit, and calculate all target values.

[0057] Step 6, referring to all the target values ​​of all the particles obtained in step 5, determine the dominance relationship between all the particles, store all the non-dominated solutions in the archive, determine the dominance relationship between all the particles in the archive, and delete all the dominated particles from the archive. In this step, it is possible that the number of particles in the archive exceeds the archive particle capacity, so another deletion operation is required. During this deletion process, in order to maintain the diversity and uniformity of the overall archive particles, the sampling crowding method is used for deletion. The crowding degree of each particle is calculated and the particles with smaller crowding degrees are deleted in turn until the archive particle capacity is reached.

[0058] Step 7: Update the historical optimal position of the particle and the objective function value. This step updates the dominant relationship between the historical optimal objective function value of the particle and the current objective value.

[0059] Step 7.1, determine whether the current particle position dominates the historical optimal position. If it dominates, update the historical optimal position of the individual particle.

[0060] Step 7.2, determine whether the individual optimal position is not dominated by the current particle position.

[0061] Step 7.3, for step 7.2, in determining whether the personal optimal position is not dominated by the current particle position, a random mechanism is introduced. A reference value is set to 0.5 in the present invention, and a random number is generated. If the random number is greater than or equal to 0.5, no update is performed. If the random number is less than 0.5, the historical optimal position of the particle is updated. This step introduces randomness, which helps to maintain the diversity of the population and prevent the algorithm from converging to a local optimal solution too early.

[0062] Step 8: Repeat steps 4 to 8 until the maximum number of iterations Gmax=1000 is reached.

[0063] Step 9: Output the archived particles, which now represent the found Pareto optimal solution and the Pareto optimal solution set.

[0064] Step 9.1, according to the above steps, the algorithm of the present invention has been run in MATLAB, and a set of Pareto results are shown in Table 1 below.

[0065] Table 1

[0066]

[0067] The Pareto optimal solution set obtained in step 9.2 and step 10 is brought into the system and compared with the multi-objective particle swarm algorithm before improvement. The comparison results are shown in Table 2 below.

[0068] Table 2

[0069]

[0070] The dominance rate is a measure of the coverage of one solution set (set A) over another solution set (set B). Specifically, the number of points in set B that are dominated by set A is calculated and divided by the total number of points in set B. The higher the dominance rate, the better the performance of set A relative to set B. Pareto solution space scatter plot comparison reference Figure 3 For the voltage curve of all outputs of the Pareto solution, refer to Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Fig. 9 , Fig.10 , Fig.11 , Fig.12 The final output Pareto solution set has a total of 27 Pareto solutions. In order to avoid the clutter of drawing 27 solutions on one graph, every three solutions are plotted as one graph, for a total of 9 graphs, which fully show the output voltage curve of the Pareto solution set.

[0071] Step 9.3, the Pareto optimal solution set obtained in step 9 is brought into the system and compared with the solution obtained by the ordinary particle swarm algorithm, and the reference solution space scatter points Fig.13 It can be seen that among the Pareto solutions, there is at least one set of solutions that dominates the solution obtained by the particle swarm algorithm, that is, the optimization effect is stronger than that of the particle swarm algorithm. The output voltage comparison diagram of selecting a set of dominant relationships is shown in the figure below: Fig.14 .

[0072] The present invention provides a control method for a three-phase rectifier system based on multi-objective hybrid elite particle swarm optimization. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A control method for a three-phase rectifier system based on multi-objective hybrid elite particle swarm optimization, characterized in that: The following steps are involved: Step 1, selecting performance indicators of the three-phase rectifier circuit as the objective function of the multi-objective particle swarm algorithm, the performance indicators including the stabilization time ST, overshoot OV and stabilization error SSE; Step 2, using a multi-objective particle swarm algorithm to minimize and optimize the objective function; Step 3, bringing the value of the particle obtained by initialization into the PI controller, and obtaining the required circuit value by simulating the circuit. The value of the particle obtained by initialization refers to three groups of proportional integral parameters pi and proportional differential parameters pd, expressed as (kp1, ki1, kp2, ki2, kp3, ki3); wherein kp1, ki1 respectively represent the proportional integral parameters Pi and proportional differential parameters pd of the first group of PI controllers; kp2, ki2 respectively represent the proportional integral parameters Pi and proportional differential parameters pd of the second group of PI controllers; kp3, ki3 respectively represent the proportional integral parameters Pi and proportional differential parameters pd of the third group of PI controllers; Then, three target values ​​represented by each particle are calculated respectively through three sets of target functions, wherein the three target values ​​include the stabilization time ST, the overshoot OV and the stabilization error SSE; Step 4, calculate the mutual dominance relationship of the target values ​​of all particles, find all non-dominated solutions, and store all non-dominated solutions in the archive; Step 5, analyze the archived particles, use the entropy weight method to calculate the minimum entropy particles, use the roulette method to select the particles with higher quality, use the minimum entropy particles and the particles with higher quality to represent the global optimal particles in the speed update process of the multi-objective particle algorithm, and use the minimum entropy particles and the particles with higher quality to form a mixed elite particle to represent the global optimal particle; Step 6, using the dominance method, by calculating the dominance ratio of the particle with the minimum entropy in the entire archived particles, and normalizing the ratio, so as to achieve dynamic adjustment of the flight coefficient of the hybrid elite particles; Step 7, update the particle speed and position; Step 8, repeat steps 3 to 7 until the set conditions are met; Step 9: During the update process, when the number of archived particles exceeds the set maximum capacity, the particles in the archive are deleted; Step 10, output the particles in the final archive. At this time, the solution set composed of all particles in the archive is the final Pareto solution set.

2. The method according to claim 1, characterized in that In step 1, space vector pulse width modulation SVPWM is used to obtain the parameters of the three-phase rectifier circuit, including kp1, ki1, kp2, ki2, kp3, and ki3.

3. The method according to claim 2, characterized in that In step 2, ST is the system response to reach and maintain the output voltage reference value. The time required to be within the allowable error range is defined by the threshold PCTvst, and the upper bound ub and lower bound lb of the error range are: （1）， （2）， The objective functions of ST, OV, and SSE are: The ST objective function is: （3）， Where vol(t) is the final output voltage as a function of time t; It means that for any time greater than time t , all satisfied ; The OV objective function is: （4）， The SSE objective function is: （5）， Where len is the length of time t and Ns is the number of time points in the steady-state phase.

4. The method according to claim 3, characterized in that Step 4 includes: setting a multi-objective optimization problem, the objective function is , where m is the number of objectives, for two solutions x_1 and x_2, if for all objective functions , i takes values ​​from 1 to m, the objective function of the solution x_1 is at least as good as the solution x_2, that is For all All are true, and at least one of the goals Make , then it is determined that solution x_1 dominates solution x_2, denoted as x_1 , expressed as: （6）。 5. The method according to claim 4, characterized in that In step 5, the archived particles are analyzed and the entropy weight method is used to calculate the particle with the minimum entropy, which specifically includes: Step 5-11, initialization of multi-objective particle swarm algorithm: Initialize and design the parameters of the multi-objective particle swarm algorithm, including the population size, the setting of the first generation of particles, the maximum iteration number, archiving, the capacity of the archived particles, the flight coefficient and the mutation rate; Step 5-12, use the entropy weight method to find elite particles: among the archived particles, use the entropy weight method to calculate the particle with the smallest information entropy, which specifically includes the following steps: Step 5-12-1, data input: The solution set consisting of archived particles is taken as the original matrix; the original matrix is ​​an n×3 matrix, where n represents the number of particles in the archive; Step 5-12-2, data normalization: （7）， in is the normalized element of the original matrix in row i and column j. is the i-th row and j-th column element of the original matrix, , are the minimum and maximum values ​​of the j-th column of the original matrix respectively; Step 5-12-3, calculate the probability distribution matrix: （8）， in is the i-th row and j-th column element of the probability distribution matrix, is the sum of all elements of the normalized matrix; Step 5-12-4, calculate information entropy: （9）， in is the information entropy of the jth target value; Step 5-12-5, calculate the information entropy weight: （10）， in, is the information entropy weight of the jth objective function; Step 5-12-6, calculate the normalized weight: （11）， in is the normalized weight, is the sum of all information entropy weights; Step 5-12-7, calculate the weighted value: （12）， in, is the weighted value of the i-th particle; Step 5-12-8, get the index of the minimum weighted value: L （13）， Where L is The position index of the minimum value is the index of the minimum position of the weighted information entropy.

6. The method according to claim 5, characterized in that In step 5, the roulette wheel selection is based on the fitness value of each particle to calculate the selection probability and make the selection by cumulative probability; The roulette wheel method is used to select particles with higher quality, specifically comprising: Step 5-21, calculate the selection probability: For each particle i, the selection probability for: （14）， in is the fitness value of the ith particle, and N is the total number of archived particles; Step 5-22, calculate the cumulative probability: Cumulative probability P i For implementing roulette selection: （15）， in represents the selection probability of the e-th particle, P i represents the cumulative probability of the i-th particle; Step 5-23, select particles: Generate a random number r∈[0,1] and select the smallest particle i that satisfies the following conditions: （16）， The roulette method is based on hypercube selection and includes the following steps: Step 5-24, select the hypercube: REP.quality is a matrix, where each row represents a hypercube and the quality of the hypercube. The second column of the matrix REP.quality REP.quality(:,2) contains the quality value of each hypercube; Step 5-25, calculate the selection probability of each hypercube h : （17）， Step 5-26, Select Hypercube: Use roulette wheel selection mechanism, based on The value of selects a hypercube: Generate a random number r between the set [0,1] and calculate: （18）， Where R represents a random number r that generates a A random number within the maximum range; Find the first hypervolume h_1 that satisfies the roulette condition: R≤ （19）， in represents the cumulative probability of the hypervolume h_1 meeting the roulette condition; Step 5-27, randomly select a particle H in the hypervolume h_1 as a particle with a higher mass selected by the roulette method; For all particles in the archived particles, in the solution space, since there are three objective functions, the solution space is a three-dimensional space. The entire solution space is divided and numbered using a set grid in the three-dimensional space, where each grid is a hypercube.

7. The method according to claim 6, characterized in that Step 6 includes: Step 6-1, calculate the maximum and minimum values ​​of each objective function value in the archive: For each objective value j, calculate the maximum and minimum values ​​of all solutions in the archive: ， （20）， in represents the target value of the i-th particle on the j-th objective function, , are the maximum and minimum values ​​of the jth objective function respectively; Step 6-2, calculate dominance: For each solution in the archive , calculate the dominance of the particle X with the minimum information entropy on each objective function: （21）， in It represents the value of the jth objective function of the particle X with the minimum information entropy. is the dominance of the particle X with the minimum information entropy over the i-th particle on the j-th objective function, represents the target value of particle i on the objective function j, represents the maximum value of the objective function j, Represents the minimum value of the objective function j; If the particle X with the smallest information entropy is better than particle i in the jth objective function, the dominance degree on the jth objective function is accumulated: when (twenty two), Where M is the number of objective functions, is the total dominance of particle X with the smallest information entropy over the i-th particle; Step 6-3, calculate the total dominance: Calculate the total dominance G of the particle X with the smallest information entropy over all solutions in the archive: （23）， Step 6-4, find the minimum value in the dominance: find the minimum value of the dominance of the particle X with the minimum information entropy over all solutions in the archive : （24）， Step 6-5: Calculate the ratio of minimum dominance to total dominance : （25）， Step 6-6, calculate the adaptive flight coefficient: ， （26）， in is the flight coefficient of the roulette high-mass particle, is the flight coefficient of the particle with the minimum information entropy, and C2 is the basic flight coefficient of the global optimal particle.

8. The method according to claim 7, characterized in that Step 7 includes: The particle velocity update formula is: （27）， The particle position update formula is: （28）， in represents the velocity of particle i at the next moment, represents the velocity of particle i at the current moment, is the weight ratio of particle i in the current speed update process, C1 is the flight coefficient of the individual optimal position, is the individual optimal position of particle i, represents the current position of particle i, represents the position of particle i at the next moment, is the particle with the smallest information entropy, For Roulette high quality particles, , , A random number between 0 and 1.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method as claimed in any one of claims 1 to 8 is implemented.

10. A computer device, characterized in that: include: A memory for storing instructions; A processor, configured to execute the instructions so that the computer device executes the method as claimed in any one of claims 1 to 8.

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