Performance Assessment of Meta-Heuristic Optimization Algorithms in Estimation of Structural Parameters of Squirrel Cage Induction Motor

Induction motors are so important in industry, so their protection and maintenance seem a vital issue. Continuous control of structural parameter of such motors is the way that can protect them. Appearance of a small problem in motor can change the value of structural parameter of squirrel cage induction motor, such as; resistances of stator and rotor, inductances of stator and rotor and mutual inductance. Therefore, precise estimation of these structural parameters with acceptable reliability can help to control condition of motor. In this paper, in order to estimate the structural parameters of under studied induction motor, gray box identification procedure has been used, such that using extracted data from motor (namely; values of root mean square of stator current and power factor) and meta-heuristic algorithms, which are Particle Swarm Optimization (PSO), Improved version of Particle Swarm Optimization (IPSO), Gravitational Search Algorithm (GSA), Improved version of Gravitational Search Algorithm (IGSA), Harmony Search (HS) and Simulated Annealing (SA), a model of under studied induction motor is identified. Obtained results show that meta-heuristic optimization algorithms can be a good chose for estimating the parameters of induction motor. Assessments show that implementing tradeoff between speed, accuracy and reliability based on the user’s requirement best meta-heuristic algorithm can be selected.