Optimal operation of induction motors using artificial neural network based on particle swarm optimization (PSO)

This paper presents the application of neural network based on PSO for losses and operating cost minimization control in the induction motor drives. In this paper, four strategies for induction motor speed control are proposed. Those four strategies are based on PSO and called Maximum Efficiency Strategy, Minimum Stator Current Strategy, Maximum Power Factor Strategy, and Maximum Weighted Cost Strategy. The proposed technique is based on the principle that the flux level in a machine can be adjusted to give the minimum amount of losses and minimum operating cost for a given value of speed and load torque. The main advantages of the proposed technique are; its simple structure and its straightforward maximization of induction motor efficiency and its operating cost for a given load torque. As will be demonstrated, PSO is so efficient in finding the optimum operating machine's flux level. The optimum flux level is a function of the machine load and speed requirements. Simulation results show that a considerable energy and cost savings is achieved in comparison with the conventional method of operation under the condition of constant voltage to frequency ratio and field oriented control.