An interior search algorithm based on chaotic and crossover strategies for parameter extraction of polyphase induction machines

Abstract

The accuracy of the extracted parameters is important for studying the polyphase induction motor performance and/or the motor control schemes. An investigated and improved interior search algorithm (IISA) is presented in this study for extracting the optimal values of estimated parameters of six-phase and three-phase induction motors. This investigation was carried out on two polyphase induction motors as experimental research cases, utilizing features of manufacturer's operation. The estimated parameters show the high capability regarding the performance of the desired IISA optimizer. The performance of the proposed IISA is compared with different modern optimization algorithms including the basic ISA, and other state-of-the-art approaches. Experimental verifications are validated on two polyphase induction motors, called six-phase and three-phase induction motors. The obtained results show that the proposed method is very competitive in extracting the unknown parameters of different induction motor models with a high degree of closeness to the experimental records. Moreover, various statistical tests, such as the Wilcoxon rank test, stability analysis, and convergence analysis, have been conducted to justify the performance of the proposed IISA. From all the analyses, it has been revealed that the proposed IISA is a competitive method compared to other popular state-of-the-art competitors and ISA variant with accurately identified parameters.

A Fuzzy-Based Proportional–Integral–Derivative with Space-Vector Control and Direct Thrust Control for a Linear Induction Motor

“…PLIMs have similar driving principles to poly-phase induction motors [6]. But, the control properties of PLIMS are more complicated according to the phenomenon of the end effect [7].…”