基于天体系统粒子群算法的异步电机参数辨识

针对标准粒子群优化(PSO)算法存在易早熟收敛的缺点,提出了一种基于天体系统模型的粒子群优化算法(CSPSO).在CSPSO算法中,参照天文学中的天体系统模型,将种群划分为多个相对独立的天体系统,每个系统按照自己的运行规则在不同的空间中运行,在算法的后期引入混沌优化,最终确定出优化问题的全局最优解.将CSPSO算法应用于异步电机参数辨识问题中,仿真结果表明CSPSO算法比GA算法和PSO算法具有更精确的参数辨识能力.

Induction Motor Parameter Identification Based on Celestial System Particle Swarm Optimization Algorithm

Aiming at the problem that the particle swarm optimization(PSO) algorithm tends to precocious convergence,a new algorithm of celestial system particle swarm optimization(CSPSO) is presented.With reference to the celestial system model in astronomy,the CSPSO algorithm divides the population into multiple independent celestial systems of which each and every one orbits in space in accordance with its own rules.The chaotic optimization is introduced in the later half of the algorithm to get the globe optimum solution.The CSPSO algorithm was applied to the identification of induction motor parameters,and the simulation results showed that it has higher identifiability parameters than GA and PSO algorithms.