Performance evaluation and comparison of genetic and differential evolution algorithms in determination of electrical and mechanical parameters of induction motors

In this thesis, the electrical and mechanical parameters of the dynamic model of the IM are determined by a genetic algorithm (GA) and differential evolution algorithm (DEA) in terms of single-objective current, single-objective speed, and multi-objective current and speed errors in order to realize high performance control of an induction motor (IM). The performances of the algorithms are compared with the experiments in both simulations and real-time. In the optimization processes, while four equations related to the electrical side of the dynamic IM model are used for determination of only the electrical parameters, five equations of the dynamic IM model are utilized for identification of the electrical and mechanical parameters together. The parameter values obtained by GA and DEA in these optimization processes are tested under the scenarios involving load torque variations in a wide speed range and the obtained performances of GA and DEA are compared within themselves and with each other. Moreover, unlike other studies in the literature, the performances of multi-objective DEA and GA in the determination of the dynamic IM model parameters are tested by both simulation and real-time experiments for the first time in the scope of this thesis.