ELMA based Design of SCMFIM to Improve the Efficiency and Power Factor

The squirrel-cage Induction motors (SCIMs) are approximately 80% of the overall electricity use in industrialized countries. In the agricultural and commercial sectors also, power consumption by high power SCIMs are quite substantial. On an average, the energy consumed by a motor during its life cycle is 40-80 times the initial cost of the motors. Therefore efficiency and power factor (PF) of the motor is very essential for both during design and operation. Even small increase in efficiency and power factor, improvement can make a big difference in energy savings in variable load applications. In this paper mainly focusing on optimal design of multiple stator-winding to improve efficiency and power factor of high power SCIMs during variable loads, the different flux level of stator winding are designed [4] (star-delta) according to variable loads and its performance is discussed. The Extreme Learning Machine Algorithm (ELMA) is used for the multiple stator winding design and optimization process and the obtained simulation results are compared. The importance of this work is to improve the efficiency and power factor of the high power three phase SCIMs during variable load applications, and to reduce power losses [15, 23] energy consumption in the industry and in the territory sectors