An Improved Particle Swarm Optimization for Induction Motor Parameter Determination

This paper presents a novel and efficient method to estimate the equivalent circuit parameters of three-phase induction motor from its manufacturer data for steady state analysis using improved particle swarm optimization (IPSO). The IPSO integrates the particle swarm optimization (PSO) with the chaotic sequences. The optimization problem is based on minimizing the error between the computed performance of the equivalent circuit and the manufacturer data. The application of chaotic sequences in PSO is an efficient strategy to improve the global searching capability and escape from local minima. The feasibility of the proposed method is demonstrated for two test motors, and the test results are compared with the simple PSO and classical parameter estimation methods. The simulation results show that the proposed method is capable of obtaining higher quality solutions.