Efficiency Improvement of Induction Motor using Fuzzy-Genetic Algorithm

In most industrial zones, electric energy is one of the most important energy sources. Since electrical motors are the main energy consumers of industrial factories, consumption optimization in these motors can be considered as a main option related to energy saving. One very effective way to reduce the consumption of these equipment is to use a motor speed controllers or drives. Since the loss of inductive motor has a direct relationship with motor flux, in this paper, the rotor flux vector control has been used. Due to the strength of fuzzy controllers in load failure and noise generation states, this controller has been used to adjust the drive speed. Two fuzzy logic inputs including speed error and speed variation derivative, and a fuzzy output, motor reference torque (Te*) are estimated. The genetic optimization algorithm has been used in order to improve the Efficiency and reduce the losses. As such, the drive performance in GA and Fuzzy-Genetic (FG) states is reviewed and the simulation results are presented. Finally, the obtained results in this paper have been compared to the results of FOC inductive motor with PI controller and without optimization. It can be seen that when FG method is employed, the results show a higher performance and losses are reduced up to almost 40 to 50% in different loads, and the amount of input power is also reduced up to approximately 30%.