Parameter Identification of Induction Motor Using Modified Particle Swarm Optimization Algorithm

TL;DR

This paper introduces a modified particle swarm optimization technique for accurately identifying induction motor parameters through startup current analysis, outperforming traditional methods in simulation.

Contribution

The paper proposes a novel modified PSO algorithm for induction motor parameter identification using startup current data, demonstrating improved accuracy over existing optimization methods.

Findings

Modified PSO outperforms line search, conventional PSO, and genetic algorithms.

The technique accurately captures true motor parameters.

Simulation results confirm the method's effectiveness.

Abstract

This paper presents a new technique for induction motor parameter identification. The proposed technique is based on a simple startup test using a standard V/F inverter. The recorded startup currents are compared to that obtained by simulation of an induction motor model. A Modified PSO optimization is used to find out the best model parameter that minimizes the sum square error between the measured and the simulated currents. The performance of the modified PSO is compared with other optimization methods including line search, conventional PSO and Genetic Algorithms. Simulation results demonstrate the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the modified PSO over other optimization techniques.