Analysis and Control of Induction Motor Using Soft Computing Techniques

Abstract

In the field of electrical motors, the induction motors (IMs) are mostly in use newlinenow a days. The design, performance evaluation, and application control of IMs newlineare quite important. The design of IMs is based on circuit parameters. The newlineaccurate measurement of electrical parameters like, resistance (or reactance) is newlinea tedious job. So, researchers have found IM parameter estimation as a significant newlineoptimization aspect. The disruption in the parameters of IMs are quite newlinefrequent which affect the performance evaluation and speed control of IMs. In newlinecase of substantial parameter disruption the IMs become unreliable. To deal newlinewith this complication of parameter disruption various strategies are used. The newlineproportional integral (PI) controllers and vector control are deployed to control newlinethe speed of IM. The vector control enhances the controlling ability of the IM newlineand subsequently improves its efficiency. The vector control of IM includes control newlineof magnitude and phase of each phase current and voltage. The controller newlineparameters are optimized using conventional or non-conventional approaches. newlineThough conventional mathematical techniques produce good results, yet the nonconventional newlinesoft computing techniques produce still better results for real-world newlineoptimization problems now a days. In this work, an efficient soft computing newlinestrategy namely, artificial bee colony (ABC) algorithm is considered to analyse newlineand control of IMs. To maintain an optimum balance amid exploration and newlineexploitation capability of ABC, it is modified and these efficient versions of ABC newlinealgorithm namely, disruption black hole ABC (DBHABC), logistic ABC, and newlinelandmark ABC (LMABC) are applied for parameter estimation, PI controller newlinebased speed control, and vector control of IMs respectively. newlineThe obtained outcomes reveal that ABC and its modified versions are significantly newlinebetter and competitive candidates than other state-of-art algorithms available newlinein literature. newline