A method for optimizing double stochastic PWM

Abstract

The application relates to the field of PWM modulation technology and discloses a method for optimizing double-random PWM c The double-random PWM is obtained by randomizing the carrier frequency f c and the pulse position, wherein the random distribution of the carrier frequency and the pulse position is respectively parameterized by using a Sigmoid function, the genetic algorithm is improved by using a particle swarm algorithm, the minimum value fmin of the random number Sigmoid distribution of the carrier frequency or the pulse position random number, the maximum value fmax of the carrier frequency or the pulse position random number and a curve transverse adjustment parameter sigma in the double-random PWM are optimized by using the improved genetic particle swarm algorithm; the method disperses harmonic energy to a wider range from the frequency and phase dimensions by the double-random PWM, reduces the harmonic amplitude at the switching frequency and the integer multiples thereof, the random number adopts the Sigmoid distribution, the nonlinear probability density characteristics of the Sigmoid distribution that more samples exist on the two sides and fewer samples exist in the middle are used, the carrier frequency and the pulse position are more likely to avoid the sensitive area, and the EMI peak caused by the extreme parameters in the traditional uniform distribution is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of PWM modulation technology, in particular to a method for optimizing double random PWM. BACKGROUND

[0002] Motor drive systems are widely used in industrial automation, electric vehicles, household appliances and other fields. However, power electronic devices (such as IGBT, MOSFET) in motor drive systems will generate high-frequency electromagnetic interference (EMI) during switching process, which not only affects the stability of the system itself, but also may cause interference to surrounding electronic equipment. And with the development of semiconductor power devices towards miniaturization, light weight and high performance, the switching performance of ultra-high frequency will lead to more serious EMI.

[0003] As a strategy to suppress EMI of motor drive system from noise source, pulse width modulation (PWM) technology has significant advantages in spectrum dispersion, harmonic suppression, reduction of switching noise and improvement of power density. However, due to the strict periodicity of switching period and pulse position, the traditional fixed frequency PWM technology leads to the concentration of harmonic energy at specific frequency points, resulting in high amplitude electromagnetic interference (EMI) peaks, which is difficult to meet the stringent requirements of modern power electronic systems (such as new energy inverters, high precision motor drives) on electromagnetic compatibility (EMC).

[0004] Single random PWM disperses harmonic energy by randomizing a single parameter (such as carrier frequency or pulse position), but its limitations are significant: only adjusting the frequency domain (random carrier frequency) or time domain (random pulse position), residual harmonic energy may still be locally concentrated, and the EMI peak suppression effect is limited; the dramatic random jump of a single parameter may cause transient disturbance of the load, increasing the risk of control loop instability; fixed pulse position or carrier frequency PWM is prone to parasitic parameter coupling of circuit, inducing high-frequency resonance and other problems. The double random PWM proposed in the present application randomizes the carrier frequency and pulse position jointly, disperses harmonic energy from multiple angles, and has the advantages of synergistic optimization, wide frequency distribution and enhanced dynamic stability.

[0005] The algorithm-optimized random PWM technology provides an innovative path for electromagnetic compatibility of high-performance motor drives. In terms of harmonic characteristic regulation, although the genetic algorithm (GA) has strong global exploration ability due to crossover and mutation operations, the selection pressure mechanism of the genetic algorithm easily leads to premature loss of population diversity, and the genetic algorithm has convergence stagnation in complex solution space. The particle swarm algorithm (PSO) can realize rapid neighborhood development by relying on a social cognition model, but the particle swarm algorithm is easily trapped in a local extremum trap due to sensitivity of an inertia weight parameter. The genetic particle swarm algorithm combines global search ability of the genetic algorithm and local search ability of the particle swarm algorithm, and has significant advantages in convergence speed, solution quality, robustness and search efficiency. The genetic particle swarm algorithm can effectively avoid premature convergence and adapt to complex optimization problems. Therefore, a method for optimizing double random PWM is provided to solve the above problems. SUMMARY

[0006] In view of the deficiencies of the prior art, the application provides a method for optimizing double random PWM, which has the advantages of EMI energy wideband balanced distribution, harmonic peak precise suppression, dynamic probability self-adaptive regulation, and solves the problems of insufficient high-frequency suppression of single random modulation, harmonic energy mutation caused by fixed probability distribution, and lack of dynamic optimization capability of traditional strategies.

[0007] To achieve the above object, the application provides the following technical scheme: a method for optimizing double random PWM, comprising the following steps:

[0008] S1: randomizing a carrier frequency f c and pulse position to obtain double random PWM,

[0009] wherein the random distribution of the carrier frequency and the pulse position is parameterized by using a Sigmoid function;

[0010] S2: improving the genetic algorithm by using a particle swarm algorithm;

[0011] S3: optimizing the minimum value f min of the random number Sigmoid distribution of the carrier frequency or the pulse position random number, the maximum value f max of the carrier frequency or the pulse position random number, and the curve transverse adjustment parameter σ in the double random PWM by using the improved genetic particle swarm algorithm.

[0012] Preferably, the step S1 is specifically:

[0013] The random carrier frequency PWM adopts a center-symmetric triangular carrier, and the traditional SVPWM carrier period T c is randomized by changing the carrier frequency f c to obtain a random wave carrier period T i , and the random carrier period T iCompared with the traditional SVPWM carrier period T c The relationship is:

[0014] ;

[0015] Among them, T c =1 / f c y is a random number between -1 and 1, and ΔT is the distribution range of the random number;

[0016] f ac Set the center frequency f of the carrier to be the center frequency. ac As a benchmark for frequency randomization.

[0017] Preferably, step S1 specifically includes:

[0018] Based on the randomized carrier frequency PWM, the randomized pulse is applied in each carrier period T. i The location inside;

[0019] A random offset method with a fixed starting position is adopted, using the start time of the PWM cycle (a=0) as the phase reference, and generating a random offset Δa in each cycle, where 0≤Δa <T i T i The carrier period is used to achieve the pulse within the carrier period T. i The position within is randomized.

[0020] Preferably, step S1 specifically includes:

[0021] By mapping traditional uniformly distributed random numbers using the Sigmoid function, a distribution with more samples in the two outer regions and fewer samples in the middle region is generated. The objective function expression of the algorithm is as follows:

[0022] ;

[0023] Where S(n) is a random number sample value of carrier frequency or pulse position, and n is the subscript value of the sample, where n is in... The variation is as follows: N0 is the total number of samples, f min The minimum value of the random number for carrier frequency or pulse position is the lower bound of this distribution, f. max The maximum value of the random number for carrier frequency or pulse position is the upper limit of this distribution. This is the horizontal adjustment parameter for the curve, where e is a constant.

[0024] Preferably, step S2 specifically includes:

[0025] S2.1: Set the population size, number of iterations, and carrier center frequency f. ac The range of values, the range of pulse position values, and the generation at which evolution terminates;

[0026] S2.2: initialize the population size, particle dimension and initial position and velocity of the particle swarm, the particle dimension corresponds to the random number sample value S(n) of carrier frequency or pulse position;

[0027] S2.3: make the weighted total harmonic distortion WTHD the fitness function of the particle swarm algorithm, and evaluate the particle fitness value according to the fitness function, the expression is:

[0028] ;

[0029] wherein φ is the harmonic order, V1 and Vφ are the amplitude of fundamental voltage and the amplitude of φth harmonic voltage respectively; φ

[0030] S2.4: update the velocity and position of each particle based on the particle swarm algorithm, the expression is:

[0031] ;

[0032] wherein ω is the inertia weight, V i (i)(t) is the velocity of particle i at time t, X i (i)(t) is the position of particle i at time t, C1 and C2 are the individual acceleration factor and global acceleration factor, r 1、 r2 is a random number between 0 and 1, pbest i is the individual optimal position found by particle i so far, gbest i is the global optimal position found by particle i so far;

[0033] S2.5: balance the global search and local optimization of the solution space by dynamically adjusting the inertia weight ω, and obtain the dynamic inertia weight ω', the expression is:

[0034] ;

[0035] wherein ω max max and ω min min are the maximum inertia weight and the minimum inertia weight respectively, r3 is a random number between 0 and 1, N' is the total iteration number, n' is the current iteration number, η is the function switching threshold, and the value range is (0, 1);

[0036] S2.6: select the parent according to the fitness from the population by using the tournament selection method;

[0037] Generate the β crossover parent based on the normal distribution by using the simulated binary crossover method;

[0038] Reset the individual value to realize mutation according to the probability Pm by using the polynomial mutation method; ​

[0039] replacing the population individuals with new offspring by replacement operation;

[0040] adopting the elitist strategy, recording the optimal individual of each generation and preserving it;

[0041] S2.7: selecting excellent individuals according to the fitness value to perform cross and mutation operations, generating new individuals and increasing population diversity, adjusting the mode and range of cross and mutation by using the search experience of the particle swarm algorithm to avoid the genetic algorithm falling into local optimum;

[0042] S2.8: fusing the results after the particle swarm algorithm guidance and the genetic algorithm operation to generate a new population, and in the fusion process, the best individual in the new individual in step S2.7 is reserved at the same time to ensure the overall quality of the population;

[0043] S2.9: according to the updated population, the velocity and position update formula of the particle swarm algorithm is used to update the velocity and position of the particles again to further guide the population to evolve in a better direction.

[0044] Preferably, the step S3 is specifically:

[0045] After the update of steps S2.4-S2.9, it is judged whether the preset termination condition is met, if yes, the search is terminated, and the Sigmoid distribution parameters f min , f max and curve transverse adjustment parameters σ of the optimal carrier frequency and pulse position random number are output, if not, the step S2.3 is returned.

[0046] Preferably, the termination condition includes reaching the maximum iteration number or the fitness value converging to the preset threshold.

[0047] Compared with the prior art, the present application provides a method for optimizing double random PWM, which has the following beneficial effects:

[0048] 1. The method for optimizing double random PWM disperses harmonic energy to a wider range from the frequency and phase dimensions by double random PWM, reduces the harmonic amplitude at the switching frequency and its integer multiples, and uses Sigmoid distribution for random numbers, so that the carrier frequency and pulse position are more likely to avoid sensitive areas and reduce EMI peaks caused by extreme parameters in traditional uniform distribution.

[0049] 2. The optimized double-random PWM method, by improving the genetic particle swarm algorithm, combines the advantages of the particle swarm algorithm, makes up for the defects of the genetic algorithm being easy to fall into local optimum and slow convergence, significantly improves the convergence speed and local search ability, accurately optimizes the key parameters of the random number distribution function in a wide solution space, generates the best random number distribution model of double-random PWM modulation, accurately controls the double randomization mode of carrier frequency and pulse position, makes the frequency band harmonic energy distribution of the motor system more uniform in the PWM driving process, and finally realizes effective suppression of the EMI of the motor system. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A double-random PWM schematic diagram is proposed for the optimized double-random PWM method of the application.

[0051] Figure 2 A double-random PWM schematic diagram is proposed for the optimized double-random PWM method of the application.

[0052] Figure 3 A double-random PWM schematic diagram is proposed for the optimized double-random PWM method of the application.

[0053] Figure 4 A motor drive system topology schematic diagram is proposed for the optimized double-random PWM method of the application.

[0054] Figure 5 A GAPSO algorithm optimization iteration diagram is proposed for the optimized double-random PWM method of the application.

[0055] Figure 6 A GA algorithm optimization iteration diagram is proposed for the optimized double-random PWM method of the application.

[0056] Figure 7 A GAPSO algorithm optimization distribution curve diagram is proposed for the optimized double-random PWM method of the application.

[0057] Figure 8 A GAPSO algorithm optimization distribution curve diagram is proposed for the optimized double-random PWM method of the application.

[0058] Figure 9 A traditional SVPWM line voltage FFT analysis diagram is proposed for the optimized double-random PWM method of the application.

[0059] Figure 10 A random carrier frequency PWM line voltage FFT analysis diagram is proposed for the optimized double-random PWM method of the application.

[0060] Figure 11A method for optimizing double random PWM is provided, and a double random PWM line voltage FFT analysis diagram is provided for the method;

[0061] Figure 12 A method for optimizing double random PWM is provided, and a double random PWM line voltage FFT analysis diagram is provided for the method. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0063] Please refer to Figures 1-12 A method for optimizing double random PWM, comprising the following steps:

[0064] As shown in the motor drive system topology diagram, it comprises a linear impedance network, a SiC MOSFET driver, a cable and a motor. The linear impedance network is used to isolate power supply interference and provide stable test impedance. The SiC MOSFET driver has high-frequency switching characteristics and needs PWM technology modulation to suppress EMI.

[0065] S1: randomize the carrier frequency f c and the pulse position to obtain double random PWM,

[0066] wherein the random distribution of the carrier frequency and the pulse position is parameterized by using a Sigmoid function respectively;

[0067] so that the originally concentrated harmonic energy at the switching frequency and its integer multiples is dispersed to a wider frequency band, as shown in Figure 1 The random pulse position PWM will randomly adjust the offset of the cycle start position, so that the harmonic energy is further diffused in the frequency domain, as shown in Figure 2 The double random PWM combined with the two is shown in Figure 3 .

[0068] S2: improve the genetic algorithm by using a particle swarm algorithm;

[0069] S3: optimize the minimum value f min of the random number Sigmoid distribution of the carrier frequency or the pulse position random number, the maximum value f max of the carrier frequency or the pulse position random number and the curve transverse adjustment parameter σ in the double random PWM by using the improved genetic particle swarm algorithm.

[0070] Step S1 specifically comprises:

[0071] The random carrier frequency PWM adopts a center-symmetrical triangle carrier, and the carrier frequency f c is changed to c randomize the traditional SVPWM carrier period T i , so that a random wave carrier period T i is obtained. c The relationship between the random wave carrier period T c and the traditional SVPWM carrier period T c is as follows:

[0072] ;

[0073] wherein, T ac =1 / f ac , y is a random number between -1 and 1, and ΔT is the distribution range of the random number.

[0074] f i is the center frequency of the carrier, and the center frequency f i of the carrier is set as the reference point of the frequency randomization.

[0075] Secondly, on the basis of the random carrier frequency PWM, the position of the pulse in each carrier period T i is randomized.

[0076] The random offset method with a fixed starting position is adopted, the starting time (a=0) of the PWM period is taken as the phase reference, a random offset Δa is generated in each period, wherein 0≤Δa<T i , and T min is the carrier period, so that the position of the pulse in the carrier period T max is randomized.

[0077] The traditional uniformly distributed random number is mapped through a Sigmoid function to generate a distribution with more samples in the two side regions and fewer samples in the middle region, and the algorithm objective function expression is as follows:

[0078] ;

[0079] wherein, S(n) is the random sample value of the carrier frequency or the pulse position, n is the index value of the sample, n changes in , N0 is the total number of samples, f is the minimum value of the carrier frequency or the pulse position random number, which is the lower limit of the distribution, f is the maximum value of the carrier frequency or the pulse position random number, which is the upper limit of the distribution, and e is a constant.

[0080] In step S2, the following is specifically performed:

[0081] S2.1: Set the population size, the number of iterations, the carrier center frequency f ac and the value range, the pulse position value range and the evolution termination number;

[0082] S2.2: Initialize the population size, particle dimension and initial position and velocity of the particle swarm, and the particle dimension corresponds to the random number sample value S(n) of the carrier frequency or pulse position;

[0083] S2.3: Make the weighted total harmonic distortion WTHD the fitness function of the particle swarm algorithm, and evaluate the particle fitness value according to the fitness function, and the expression is:

[0084] ;

[0085] Wherein, φ is the harmonic order, V1 and V φ are the fundamental voltage amplitude and the φth harmonic voltage amplitude respectively;

[0086] S2.4: Update the velocity and position of each particle based on the particle swarm algorithm, and the expression is:

[0087] ;

[0088] Wherein, ω is the inertia weight, V i (t) is the velocity of particle i at time t, X i (t) is the position of particle i at time t, C1 and C2 are the individual acceleration factor and the global acceleration factor, r 1、 r2 is a random number between 0 and 1, pbest i is the individual optimal position found by particle i so far, and gbest i is the global optimal position found by particle i so far;

[0089] S2.5: Balance the global search and local optimization of the solution space by dynamically adjusting the inertia weight ω, and get the dynamic inertia weight ω', and the expression is:

[0090] ;

[0091] Wherein, ω max , ω min are the maximum inertia weight and the minimum inertia weight respectively, r3 is a random number between 0 and 1, N' is the total number of iterations, n' is the current iteration number, η is the function switching threshold, and the value range is (0, 1);

[0092] S2.6: Select the tournament selection method, and select the parent according to the fitness from the population;

[0093] Generate the β crossover parent based on the normal distribution using the simulated binary crossover method;

[0094] In the polynomial mutation method, the individual value is reset according to the probability Pm to realize mutation;

[0095] The population individuals are replaced by new offspring through the replacement operation;

[0096] The elite reservation strategy is adopted to record and reserve the optimal individual of each generation;

[0097] S2.7: Select the excellent individual according to the fitness value to perform the crossover and mutation operations, generate new individuals and increase the population diversity, adjust the mode and range of crossover and mutation by using the search experience of the particle swarm algorithm, and avoid the genetic algorithm from falling into local optimum;

[0098] S2.8: Fuse the results after the particle swarm algorithm guidance and the genetic algorithm operation to generate a new population, and reserve the best individual in the new individual in the fitness step S2.7 during the fusion process to ensure the overall quality of the population;

[0099] S2.9: According to the updated population, the velocity and position update formula of the particle swarm algorithm is used to update the velocity and position of the particles, and the population is further guided to evolve in a better direction.

[0100] Step S3 is specifically:

[0101] After the update of steps S2.4-S2.9, it is judged whether the preset termination condition is met, if yes, the search is terminated, and the optimal carrier frequency and the Sigmoid distribution parameters f min , f max and the curve transverse adjustment parameter σ of the random number of pulse position are output, if not, the step S2.3 is returned.

[0102] The termination conditions include reaching the maximum number of iterations or the fitness value converging to the preset threshold.

[0103] The algorithm iteration results are as follows: the GA algorithm optimization iteration diagram is shown in Figure 6 , from which it can be seen that the algorithm converges rapidly in the first 200 generations but falls into local optimum in the 200th to 600th generations, and the final fitness value is 0.3040. The GAPSO algorithm optimization iteration diagram is shown in Figure 5 , from which it can be seen that the algorithm converges more uniformly in the whole iteration process, and the final fitness value is 0.2488. It can be obtained that the GA algorithm with the PSO algorithm can accelerate the convergence speed and enhance the local search ability.

[0104] The distribution curve after the GAPSO algorithm optimization is shown in Figures 7-8 , wherein Figure 7 is the carrier frequency distribution curve diagram after optimization, Figure 8To optimize the post-pulse position distribution curve, it can be seen that both present the characteristics of Sigmoid distribution, that is, the samples are dense on both sides and less in the middle.

[0105] The optimal sample distribution data after algorithm optimization is imported into MATLAB / Simulink for simulation verification. First, run the traditional SVPWM, random carrier frequency PWM and double random PWM simulation model, and the obtained line voltage FFT analysis diagram is as shown in Figures 9-12 It can be seen that the harmonic components of the traditional SVPWM are concentrated at the switching frequency and its integer multiples, with high harmonic peaks, and the maximum harmonic amplitude can reach 37.61, and the THD is 80.18%. The random carrier frequency PWM increases the fundamental amplitude from 203.5 to 213, but reduces the THD from 80.18% to 78.35%, and the harmonic amplitude peak is suppressed from 37.61 to 4.69.

[0106] On this basis, the random pulse position PWM is added, and Figure 11 It can be seen that the harmonic components are dispersed to the random number distribution range of the switching frequency and its integer multiples, the fundamental amplitude is slightly reduced to 212.9, the THD is slightly reduced to 76.39%, and the harmonic amplitude peak is further suppressed from 4.69 to 4.47. The harmonic components at the switching frequency and its integer multiples are further reduced, but the harmonic components in the non-random number distribution range are increased. The effect of the proposed strategy is shown in Figure 12 The amplitude is slightly reduced to 212.7, the THD is slightly reduced to 76.36%, and the harmonic peak amplitude is reduced from 4.47 to 3.19. The proposed method has the effect of reducing the superposition of each harmonic in the frequency domain range, so that the energy originally concentrated at the switching frequency and its multiple harmonic frequencies is more evenly distributed in the entire frequency domain range, further improving the shortcomings of the traditional random PWM.

[0107] In summary, the optimized double random PWM method disperses the harmonic energy to a wider range from the frequency and phase dimensions by double random PWM, reduces the harmonic amplitude at the switching frequency and its integer multiples, and uses Sigmoid distribution for random numbers. The nonlinear probability density characteristics of the samples on both sides and less in the middle make it easier for the carrier frequency and pulse position to avoid sensitive areas and reduce EMI peaks caused by extreme parameters in the traditional uniform distribution.

[0108] And, the improved genetic particle swarm algorithm fuses the advantages of the particle swarm algorithm, makes up for the defects of the genetic algorithm of being easy to fall into local optimum and slow convergence, significantly improves the convergence speed and local search ability, accurately searches the key parameters of the random number distribution function in a wide solution space, generates the best random number distribution model of the double random PWM modulation, and through the double randomization mode of accurately regulating the carrier frequency and pulse position, makes the frequency band harmonic energy distribution of the motor system more uniform in the PWM driving process, and finally realizes the effective suppression of the EMI of the motor system.

[0109] It should be noted that the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises... " does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0110] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, alternatives, and variations can be made in the embodiments without departing from the spirit and scope of the present application as defined by the appended claims and their equivalents.

Claims

1. A method for optimizing dual random PWM, characterized in that, Includes the following steps: S1: Randomized carrier frequency f c The pulse position is used to obtain dual random PWM. The random distribution of carrier frequency and pulse position is set using the Sigmoid function. S2: Improve the genetic algorithm using the particle swarm optimization algorithm; S3: The improved genetic particle swarm optimization algorithm is used to find the minimum value f of the carrier frequency or pulse position random numbers in the Sigmoid distribution of the dual random PWM. min The maximum value f of the random number of carrier frequency or pulse position. max Optimize the horizontal adjustment parameter σ of the curve; After completing the update in step S2, determine whether the preset termination condition is met. If it is met, terminate the search and output the Sigmoid distribution parameters f of the random numbers of the optimal carrier frequency and pulse position. min f max The horizontal adjustment parameter σ of the curve is not satisfied. If it is not satisfied, the weighted total harmonic distortion WTHD is re-set as the fitness function of the particle swarm algorithm, and the particle fitness value is evaluated according to the fitness function. Step S1 is as follows: Uniformly distributed random numbers are mapped using the Sigmoid function to generate a distribution with more samples in the two outer regions and fewer samples in the middle region. The objective function expression of the algorithm is defined as follows: Where S(n) is a random number sample value of carrier frequency or pulse position, and n is the subscript value of the sample, where n is in... The variation is as follows: N0 is the total number of samples, f min The minimum value of the random number for carrier frequency or pulse position is the lower bound of this distribution, f. max The maximum value of the random number for carrier frequency or pulse position is the upper limit of this distribution. This is the horizontal adjustment parameter for the curve, where e is a constant.

2. The method for optimizing dual random PWM according to claim 1, characterized in that, Step S1 specifically involves: Random carrier frequency PWM uses a centrally symmetrical triangular carrier, and the carrier frequency f is changed. c To adjust the SVPWM carrier period T c Randomization yields the random wave carrier period T. i Random carrier period T i With SVPWM carrier period T c The relationship is: Among them, T c =1 / f c y is a random number between -1 and 1, and ΔT is the distribution range of the random number; f ac Set the center frequency f of the carrier to be the center frequency. ac As a benchmark for frequency randomization.

3. The method for optimizing dual random PWM according to claim 1, characterized in that: Step S1 specifically involves: Based on the randomized carrier frequency PWM, the randomized pulse is applied in each carrier period T. i The location inside; A random offset method with a fixed starting position is adopted, using the start time of the PWM cycle (a=0) as the phase reference, and generating a random offset Δa in each cycle, where 0≤Δa <T i T i The carrier period is used to achieve the pulse within the carrier period T. i The position within is randomized.

4. The method for optimizing dual random PWM according to claim 1, characterized in that, Specifically, step S2 involves: S2.1: Set the population size, number of iterations, and carrier center frequency f. ac The range of values, the range of pulse position values, and the generation at which evolution terminates; S2.2: Initialize the population size, particle dimension, and initial position and velocity of the particle swarm, wherein the particle dimension corresponds to the random number sample value S(n) of the carrier frequency or pulse position. S2.3: Weighted Total Harmonic Distortion (WTHD) is the fitness function of the particle swarm optimization algorithm. The particle fitness value is evaluated based on this fitness function, expressed as: Where φ is the harmonic order, V1, V φ These are the fundamental voltage amplitude and the φth harmonic voltage amplitude, respectively. S2.4: Update the velocity and position of each particle based on the particle swarm optimization algorithm, expressed as: Where ω is the inertia weight, V i (t) represents the velocity of particle i at time t, X i (t) represents the position of particle i at time t, C1 and C2 are the individual acceleration factor and the global acceleration factor, respectively, and r 1、 r2 is a random number between [0,1], pbest i gbest is the optimal position found so far for particle i. i Let i be the globally optimal position found so far. S2.5: By dynamically adjusting the inertia weight ω, the global search and local optimization of the solution space are balanced, and the dynamic inertia weight ω' is obtained, with the expression as follows: Where, ω max ω min These are the maximum and minimum inertia weights, respectively; r3 is a random number in (0,1); N' is the total number of iterations; n' is the current number of iterations; and η is the function switching threshold, with a value range of (0,1). S2.6: Use tournament selection method to select parents from the population based on fitness; Using a simulated binary crossover method, β-crossover parents are generated based on a normal distribution; The mutation is achieved by resetting the individual values ​​according to the probability Pm using the polynomial mutation method. The population is replaced with new offspring through a replacement operation; An elite retention strategy is adopted, recording and retaining the best individuals in each generation; S2.7: Select excellent individuals based on fitness values ​​for crossover and mutation operations to generate new individuals and increase population diversity. Utilize the search experience of particle swarm optimization to adjust the method and scope of crossover and mutation to avoid the genetic algorithm getting stuck in local optima. S2.8: Merge the results after the particle swarm optimization algorithm and the genetic algorithm to generate a new population. During the fusion process, the best individual from the new individuals in fitness step S2.7 is retained to ensure the overall quality of the population. S2.9: Based on the updated population, the velocity and position of the particles are updated again using the velocity and position update formulas of the particle swarm optimization algorithm, further guiding the population to evolve in a better direction.

5. The method for optimizing dual random PWM according to claim 1, characterized in that, The termination conditions include reaching the maximum number of iterations or the fitness value converging to a preset threshold.

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