Performance study of Mine Blast Algorithm for automatic voltage regulator tuning

S. Majumdar, K. Mandal, N. Chakraborty
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引用次数: 17

Abstract

Proportional Integral Derivative (PID) controllers are extensively used in industry for process instrumentation application. PID controllers have also found widespread application in Power System Control. To achieve effective control optimal tuning of PID gains of the controller is necessary. This controller gain tuning problem is a multimodal non-convex optimization problem. This paper proposes a tuning strategy based on Mine Blast Algorithm (MBA); a population based algorithm for tuning the controller. This algorithm is a newly developed optimization technique. The motivation of this study is to determine if MBA presents a better alternative than traditional soft computing based optimization methods. The algorithm simulates the behavior of exploding mines in a mine field. This algorithm has been used to find optimal values of PID gains. The performance of MBA is compared with results obtained from Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). All the codes have been developed in house in Matlab environment. MBA has demonstrated up to 40% reduction in computational burden while maintaining controllers output characteristics.
矿井爆破自动调压算法的性能研究
比例积分导数(PID)控制器广泛应用于工业过程仪表。PID控制器在电力系统控制中也得到了广泛的应用。为了实现有效的控制,需要对控制器的PID增益进行最优整定。该控制器增益整定问题是一个多模态非凸优化问题。本文提出了一种基于矿井爆破算法(MBA)的优化策略;一种基于种群的控制器调优算法。该算法是一种新兴的优化技术。本研究的动机是确定MBA是否比传统的基于软计算的优化方法提供更好的选择。该算法模拟了地雷在雷区爆炸时的行为。该算法已被用于寻找PID增益的最优值。将该算法的性能与粒子群算法(PSO)和遗传算法(GA)进行了比较。所有的代码都是在Matlab环境下自行开发的。MBA已经证明,在保持控制器输出特性的同时,计算负担减少了40%。
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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