Studies on efficiency and power factor optimization of IM using soft computing

Induction Machines (IM) are widely employed as actuators in electromechanical driven system, and IM's control problems, the demands of which are to realize energy saving and speed regulation, have become more significant than before. Efficiency and power factor are two crucial indices in energy saving control issues, and to realize economical running with remarkable efficiency and high power factor, optimization of efficiency and power factor are necessary. From the theoretical points of view, the two indices conflicts with each other in a certain conditions and the conventional solutions to these problems can be attributed to steady state optimization by means of rigid calculations, the results of which often lead to neglect of parameters' perturbations and lack of adaptation and robustness. Comparing with traditional control decisions, this paper analyses steady state model and dynamical model of IM, indicates that the courses of optimize efficiency and power factor can be regarded as to solve a kind of speed regulation issues with multiple parameters and conditions' restrictions, and proposes a new control method on the basis of soft computing. Simulation results show two indices follow their optimal points on the conditions of parameters' perturbations, and state variables' curves approach nearer to the optimal point than PID strategies. It has provided a theoretical support to practical applications.