Implementation and Evaluation of Grey Wolf optimization Algorithm on Power System Stability Enhancement

A. Alahmed, Salman U. Taiwo, M. A. Abido
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引用次数: 1

Abstract

The increasing complexity of today’s applications has surfaced the importance of meta-heuristic techniques which can deal with multi-variable, multi-constraints, highly non-linear and non-smooth problems. Their superior performance and immunity of getting trapped in local maxima or minima made them eminent when compared with classical optimization methods, which have several limitations. In this context, implementation and evaluation of Grey Wolf optimization Algorithm (GWOA) on power system stability enhancement will be carried. The objective function is to maximize the minimum damping ratio of the controller to enhance stability and ensure faster damping. The results will be then compared with other evolutionary techniques, particularly Real-coded Genetic Algorithm (RCGA) and Differential Evolution (DE) method. The simulation results will be established using MATLAB.
灰狼优化算法在电力系统稳定性增强中的实现与评价
随着当今应用的日益复杂,元启发式技术的重要性日益凸显,它可以处理多变量、多约束、高度非线性和非光滑问题。与传统的优化方法相比,该方法具有较强的性能和较强的抗局部极值能力,具有一定的局限性。在此背景下,本文将对灰狼优化算法(GWOA)在增强电力系统稳定性方面的实现和评价进行研究。目标函数是使控制器的阻尼比最小最大化,以增强稳定性并保证更快的阻尼。然后将结果与其他进化技术进行比较,特别是实编码遗传算法(RCGA)和差分进化(DE)方法。利用MATLAB建立仿真结果。
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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