Optimization method of structural parameters of switched reluctance motor based on adaptive fruit fly algorithm

Abstract

The invention discloses a switched reluctance motor structure parameter optimization method based on an adaptive fruit fly algorithm. The method comprises the steps that switched reluctance motor stator pole arc and rotor pole arc structure parameters serve as optimization objects, the motor efficiency and the torque ripple coefficient serve as optimization objects, and by achieving the collaborative optimal efficiency and torque ripple coefficient, the optimal motor structure parameters are obtained. The method has the advantages that optimizing is conducted through a dual population fruit fly algorithm, the searching capability can be effectively enhanced, and the optimization efficiency is improved; random-angle optimizing within the limited range can be given to individual fruit flies, the problem that when optimizing is conducted through a traditional fruit fly algorithm, jumping out of the stator pole arc and rotor pole arc limited border is likely to occur is solved, and the convergence speed is increased; the individual fruit flies adopt a decreasing circle optimizing mode, and the phenomenon of falling into a local optimal solution can be effectively avoided. In a word, the method has the advantages of being high in optimizing speed, high in operation efficiency, not likely to fall into the local optimal solution and the like.

Description

technical field [0001] The invention relates to the technical field of switched reluctance motors, in particular to a method for optimizing structural parameters of switched reluctance motors based on an adaptive fruit fly algorithm. Background technique [0002] Switched reluctance motors have been widely used in many fields such as electric vehicles and wind power because of their simple and firm structure, flexible control, and wide speed range. However, the large torque ripple and low operating efficiency have affected its popularization and application, so how to improve the above technical performance has become the focus of common attention of researchers at home and abroad. The non-sinusoidal nature of the winding current and the high saturation of the core magnetic flux density make the switched reluctance motor a multi-variable, strongly coupled nonlinear system, which brings great difficulties to design and optimization. At present, domestic and foreign researche...

AI Technical Summary

Problems solved by technology

For example: simulated annealing algorithm has slow convergence speed and long execution time; genetic algorithm is prone to premature, poor stability, and small processing scale; particle swarm collaborative optimization algorithm has many adjustment parameters, heavy workload, and low optimization efficiency, etc.

The Drosophila algorithm is a new method based on the foraging behavior of Drosophila to deduce the optimal global optimization. Compared with genetic and particle swarm algorithms, this method has the advantages of less parameter settings and high operating efficiency; however, this method is applied to switches. In terms of the optimization of the structural parameters of the reluctance motor, it is easy to jump out of the limit boundary when optimizing in the stator and rotor pole arc limit conditions, which affects its convergence speed, and the fixed step size in the fruit fly algorithm is sensitive to search efficiency and is not easy to choose, which affects its operation efficiency

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