Stochastic optimization algorithms for parameter identification of three phase induction motors with experimental verification

Abstract

Induction motors are widely used in industrial processes owing to their efficiency, adaptability, and low cost. For controlling, analyzing, and solving induction motor problems, the estimation of the equivalent circuit parameters is essential. Recently, numerous metaheuristics have been employed to estimate the induction motor parameters, due to their robustness, simplicity, and rapidity. In this paper, six different metaheuristics are used to estimate the parameters of an induction motor: particle swarm optimization (PSO), stochastic fractal search (SFS), equilibrium optimizer (EO), manta ray foraging optimization (MRFO), chaos game optimization (CGO), and jellyfish search (JS). The estimation accuracy of these metaheuristics is verified by computing the sum of the absolute differences between the measured and equivalent model outputs.Experimental results show that the CGO algorithm can provide a small SAD value of 1.1045, and SFS methods can also yield an acceptable SAD value up to 1.1063.