A Hybrid Self-Optimizing Simulated Annealing and Particle Swarm Optimization Approach for PMSM Parameter Optimization

In the power system, permanent magnet synchronous motors are an important component of the key assets in power grid companies. To improve the operational efficiency of permanent magnet synchronous motors and reduce maintenance costs, a parameter optimization method combining self-optimizing Simulated Annealing (SA) and Particle Swarm Optimization (PSO) is designed. This method utilizes the powerful global search performance of SA to avoid local optimal solutions, and combines the fast convergence characteristics of PSO to achieve precise and efficient parameter tuning. At the same time, greedy optimization strategy and memory tempering mechanism are introduced into the PSO. A self-optimizing strategy based on SA and PSO is designed. The specific method used in the study is to combine the powerful global search ability of SA algorithm with the fast convergence characteristics of PSO algorithm, integrate the advantages of both algorithms, and achieve fast and accurate identification of motor parameters. By incorporating greedy optimization strategies and memory tempering mechanisms within the PSO framework, the limitations of insufficient accuracy in handling multivariate parameter identification problems can be addressed. From the results, the parameters of the permanent magnet synchronous motor optimized by the self-optimizing resulted in a direct axis inductance error of 0.62%, a quadrature axis inductance error of 0.31%, a resistance error of 0.34%, and a magnetic linkage error of 5.14%. In addition, the standard deviation of the self-optimizing SA-PSO was 0.04, which was 0.15, 0.10, and 0.06 lower than the standard deviations of PSO, SA, and SA-PSO algorithms of 0.19, 0.14, and 0.10, respectively. In terms of stability, the standard deviation of the hybrid strategy was 0.012, which was 73.33%, 69.23%, and 57.14% lower than PSO, SA, and traditional SA-PSO, respectively. Therefore, the permanent magnet synchronous motor parameter system optimized by combining self-optimizing SA with PSO effectively reduces energy consumption during operation, which helps the power grid company to achieve dual benefits in economic benefits and environmental sustainability.