Application of Adaptive Population Optimization Artificial Fish School Algorithm to the Identification of Induction Motor Parameters

Abstract

This paper introduces the selection operator, crossover operator and mutation operator of genetic algorithm into artificial fish school algorithm, and perform different operations on some artificial fish individuals respectively. The paper realizes the organic fusion of the two algorithms, and designs the step-length field of vision adjustment mechanism, and propose an optimized adaptive artificial fish school algorithm to the identification of induction motor parameters. The novel algorithm combines the advantages of the above two algorithms to improve the efficiency and accuracy of induction motor parameter identification. The algorithm was applied in the process of identifying load parameters of induction motors in an actual power system in a province in southeast China, and the results show that the algorithm has unique advantages in accelerating identification speed, improving identification accuracy and parameter stability.