New technique for Maximum Efficiency and Minimum Operating Cost of induction motors based on Particle Swarm Optmization (PSO)

R.H.A. Hamid, A. Amin, R. Ahmed, A. El-Gammal
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引用次数: 12

Abstract

This paper presents the application of PSO for losses and operating cost minimization control in the induction motor drives. In this paper, two strategies for induction motor speed control are proposed. Those two strategies are based on PSO and called maximum efficiency strategy and minimum operating cost strategy. The proposed technique is based on the principle that the flux level in a machine can be adjusted to give the minimum amount of losses and minimum operating cost for a given value of speed and load torque. The main advantages of the proposed technique are; its simple structure and its straightforward maximization of induction motor efficiency and its operating cost for a given load torque. As will be demonstrated, PSO is so efficient in finding the optimum operating machines flux level. The optimum flux level is a function of the machine load and speed requirements. Simulation results show that a considerable energy and cost savings is achieved in comparison with the conventional method of operation under the condition of constant voltage to frequency ratio.
基于粒子群优化(PSO)的感应电机效率最大化和运行成本最小化新技术
本文介绍了粒子群算法在异步电动机传动中损耗和运行成本最小化控制中的应用。本文提出了两种异步电机速度控制策略。这两种策略都是基于粒子群算法的,称为最大效率策略和最小运营成本策略。所提出的技术是基于这样的原理,即在给定的速度和负载转矩值下,可以调整机器中的磁通水平,使损耗最小,运行成本最小。该技术的主要优点是:其简单的结构和它的直接最大化的感应电机效率和其运行成本的给定负载转矩。正如将要证明的那样,粒子群算法在寻找最佳操作机器磁通水平方面是如此有效。最佳通量水平是机器负载和速度要求的函数。仿真结果表明,在恒压频比条件下,与传统的运行方法相比,该方法节省了大量的能量和成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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