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

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

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-ar...

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Main Authors: Hossam E.A. Talaat, Adel Abdennour, Abdulaziz A. Al-Sulaiman
Format: Article
Language:English
Published: Elsevier 2010-07-01
Series:Journal of King Saud University: Engineering Sciences
Online Access:http://www.sciencedirect.com/science/article/pii/S1018363918305002
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spelling doaj-dcd7211a871e41f49c65314be81ecab72020-11-25T02:27:40ZengElsevierJournal of King Saud University: Engineering Sciences1018-36392010-07-01222129137A GA-optimized Neuro-fuzzy Power System Stabilizer for Multi-machine SystemHossam E.A. Talaat0Adel Abdennour1Abdulaziz A. Al-Sulaiman2Electrical Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaElectrical Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaElectrical Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaThe 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. Keywords: Distributed control, Fuzzy neural networks, Genetic algorithms, Power system stabilityhttp://www.sciencedirect.com/science/article/pii/S1018363918305002
collection DOAJ
language English
format Article
sources DOAJ
author Hossam E.A. Talaat
Adel Abdennour
Abdulaziz A. Al-Sulaiman
spellingShingle Hossam E.A. Talaat
Adel Abdennour
Abdulaziz A. Al-Sulaiman
A GA-optimized Neuro-fuzzy Power System Stabilizer for Multi-machine System
Journal of King Saud University: Engineering Sciences
author_facet Hossam E.A. Talaat
Adel Abdennour
Abdulaziz A. Al-Sulaiman
author_sort Hossam E.A. Talaat
title A GA-optimized Neuro-fuzzy Power System Stabilizer for Multi-machine System
title_short A GA-optimized Neuro-fuzzy Power System Stabilizer for Multi-machine System
title_full A GA-optimized Neuro-fuzzy Power System Stabilizer for Multi-machine System
title_fullStr A GA-optimized Neuro-fuzzy Power System Stabilizer for Multi-machine System
title_full_unstemmed A GA-optimized Neuro-fuzzy Power System Stabilizer for Multi-machine System
title_sort ga-optimized neuro-fuzzy power system stabilizer for multi-machine system
publisher Elsevier
series Journal of King Saud University: Engineering Sciences
issn 1018-3639
publishDate 2010-07-01
description 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. Keywords: Distributed control, Fuzzy neural networks, Genetic algorithms, Power system stability
url http://www.sciencedirect.com/science/article/pii/S1018363918305002
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