A GA-optimized Neuro-fuzzy Power System Stabilizer for Multi-machine System

被引：0

作者：

Talaat H.E.A. ^{[1
]}

Abdennour A. ^{[1
]}

Al-Sulaiman A.A. ^{[1
]}

机构：

[1] Electrical Engineering Department, College of Engineering, King Saud University, Riyadh

来源：

Journal of King Saud University - Engineering Sciences | 2010年 / 22卷 / 02期

关键词：

Distributed control; Fuzzy neural networks; Genetic algorithms; Power system stability;

D O I：

10.1016/S1018-3639(18)30500-2

中图分类号：

学科分类号：

摘要：

The aim of this research is the design of a decentralized Power System Stabilizer (PSS) capable of performing well for a wide range of variations in system parameters and loading conditions. In addition, the designed PSS should provide effective damping of small/large disturbances and local/inter-area oscillations. The framework of the design is based on Fuzzy Logic Control (FLC). In particular, the neuro-fuzzy control rules are derived from training three classical PSSs; each is tuned using GA (Genetic Algorithms) so as to perform optimally at one operating point. The effectiveness and robustness of the designed stabilizer is investigated. The results of simulation prove that the proposed PSS offers a superior performance in comparison with the conventional stabilizer presently adopted by the industry. © 2010 King Saud University

引用

页码：129 / 137

页数：8

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