基于遗传算法的十一相感应电机故障电流优化

A. Ashoush, S. Gadoue, A. Abdel-Khalik, A. L. Mohamadein
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引用次数: 18

摘要

本文提出了一种基于遗传算法(GA)的优化技术,用于计算多相感应电机在缺相情况下的最佳相电流,以保持与正常情况相同的磁动机分布。传统的优化方法需要求解复杂的非线性方程。此外,约束条件的选择应使已解方程的个数等于变量的个数,从而得到唯一解。当断开相数超过两个时,问题就变得复杂了。由于遗传算法不涉及求解非线性方程,因此可以采用遗传算法求解这类优化问题。采用有限元分析方法,模拟了每一种最优解对应的机器磁链,对传统优化方法与遗传算法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Current optimization for an eleven-phase induction machine under fault conditions using Genetic Algorithm
In this paper, an optimization technique based on Genetic Algorithm (GA) is proposed to calculate the optimum phase currents of a multi-phase induction machine under phase(s) loss to maintain the same Magneto-Motive Force (MMF) distribution as with the healthy case. Conventional optimization method requires solving complex nonlinear equations. Moreover, constraints should be selected such that the number of solved equations equals the variables number to obtain a unique solution. The problem becomes complicated as the number of disconnected phases is more than two. Genetic algorithm is used to solve such optimization problem since it does not involve solving nonlinear equations. Comparison between the conventional optimization technique and GA is carried out using finite element analysis which simulates the machine flux corresponding to each optimum solution.
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