Bio-inspired optimization algorithms for parameter determination of three-phase induction motor
Purpose - The aim of this research paper is to examine the bio-inspired optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithm with adaptive chemotactic step for determining the steady-state equivalent circuit parameters of the three-phase induction motor using a set of manufacturer data. Design/methodology/approach - The induction motor parameter determination issue is devised as a nonlinear constrained optimization problem. The nonlinear equations of various quantities (torque, current and power factor) are derived in terms of equivalent circuit parameters from a single and a double-cage model, and then, equates to the corresponding manufacturer data. These equations are solved by the bio-inspired algorithms. Using the squared error between the determined and the manufacturer data as the objective function, the parameter determination problem is transferred into an optimization process where the model parameters are determined that minimize the defined objective function. The objective function is iteratively minimized using GA, PSO and BFO techniques. In order to balance the exploration and exploitation searches of the BFO algorithm, an adaptive chemotactic step is utilized. Findings - Comparisons of the results of GA, PSO, BFO and IEEE Std. 112-F (using no-load, locked-rotor and stator resistance tests) methods for two sample motors are presented. Results show the superiority of the bio-inspired optimization algorithms over the classical one. Besides, BFO-based parameter determination method is observed to obtain better quality solutions quickly than GA and PSO methods. Practical implications - The parameters obtained by the proposed approaches can be used in analyzing the stalling and/or reacceleration process of a loaded motor following a fault or during voltage sag condition as well as in system-level studies. Originality/value - The most significant contribution of the research is the potential to determine the equivalent circuit parameters, of induction motor only from its manufacturer data without conducting any lab tests on the motor. The bio-inspired optimization based parameter determination approaches are faster and less intrusive than the IEEE Std. 112-F method.
parameter determination induction motor genetic algorithms circuits bio-inspired algorithms sensitivity analysis