Static security assessment in deregulated power system using artificial inteligence techniques

The basic function of an electric power system is to supply customers with electric energy as economically as possible and with a reasonable degree of continuity and quality. Deregulation of power system in recent years has turned static security assessment (SSA) into a challenging task for which ac...

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Main Author:	Ali Ilsaeh, Ibrahem Salem
Format:	Thesis
Language:	English
Published:	2009
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/12708/1/IbrahemSalemAliMFKE2009.pdf
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id	my-utm-ep.12708
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spelling	my-utm-ep.127082018-06-25T08:59:33Z Static security assessment in deregulated power system using artificial inteligence techniques 2009 Ali Ilsaeh, Ibrahem Salem TK Electrical engineering. Electronics Nuclear engineering The basic function of an electric power system is to supply customers with electric energy as economically as possible and with a reasonable degree of continuity and quality. Deregulation of power system in recent years has turned static security assessment (SSA) into a challenging task for which acceptably fast and accurate assessment methodology is essential. The objective of this research is to investigate the reliability of the SSA in determining the security level of power system from serious interference during operation. In this research, three types of Artificial Intelligence (AI) techniques namely Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System and Decision Trees are implemented to classify the security status in the test power system, comparison are made in terms of computation time and accuracy of the networks. Data obtained from Newton Raphson Load Flow (NRLF) analysis method are used for the training and testing purposes of the proposed AI techniques. The data are used also as a benchmark to validate the results from AI techniques to achieve high speed of execution and good classification accuracy. A new methodology of feature selection technique based on extracting variables has also been applied. The proposed techniques have been extended and tested on 5, 30, 57 and 118 IEEE test systems. The deregulated system is configured into base case, pool and bilateral contract modes in order to evaluate the effectiveness of the proposed techniques for SSA on deregulated power system. Generally, the proposed AI techniques have successfully been applied to evaluate SSA for various IEEE test system configured as deregulated systems. These techniques are as accurate as NRLF techniques but with shorter computation time. It is found that the ANN is well suited for online SSA of deregulated power systems amongst the three methods applied. 2009 Thesis http://eprints.utm.my/id/eprint/12708/ http://eprints.utm.my/id/eprint/12708/1/IbrahemSalemAliMFKE2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Ali Ilsaeh, Ibrahem Salem Static security assessment in deregulated power system using artificial inteligence techniques
description	The basic function of an electric power system is to supply customers with electric energy as economically as possible and with a reasonable degree of continuity and quality. Deregulation of power system in recent years has turned static security assessment (SSA) into a challenging task for which acceptably fast and accurate assessment methodology is essential. The objective of this research is to investigate the reliability of the SSA in determining the security level of power system from serious interference during operation. In this research, three types of Artificial Intelligence (AI) techniques namely Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System and Decision Trees are implemented to classify the security status in the test power system, comparison are made in terms of computation time and accuracy of the networks. Data obtained from Newton Raphson Load Flow (NRLF) analysis method are used for the training and testing purposes of the proposed AI techniques. The data are used also as a benchmark to validate the results from AI techniques to achieve high speed of execution and good classification accuracy. A new methodology of feature selection technique based on extracting variables has also been applied. The proposed techniques have been extended and tested on 5, 30, 57 and 118 IEEE test systems. The deregulated system is configured into base case, pool and bilateral contract modes in order to evaluate the effectiveness of the proposed techniques for SSA on deregulated power system. Generally, the proposed AI techniques have successfully been applied to evaluate SSA for various IEEE test system configured as deregulated systems. These techniques are as accurate as NRLF techniques but with shorter computation time. It is found that the ANN is well suited for online SSA of deregulated power systems amongst the three methods applied.
format	Thesis
qualification_level	Master's degree
author	Ali Ilsaeh, Ibrahem Salem
author_facet	Ali Ilsaeh, Ibrahem Salem
author_sort	Ali Ilsaeh, Ibrahem Salem
title	Static security assessment in deregulated power system using artificial inteligence techniques
title_short	Static security assessment in deregulated power system using artificial inteligence techniques
title_full	Static security assessment in deregulated power system using artificial inteligence techniques
title_fullStr	Static security assessment in deregulated power system using artificial inteligence techniques
title_full_unstemmed	Static security assessment in deregulated power system using artificial inteligence techniques
title_sort	static security assessment in deregulated power system using artificial inteligence techniques
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2009
url	http://eprints.utm.my/id/eprint/12708/1/IbrahemSalemAliMFKE2009.pdf
_version_	1747814949744279552

Static security assessment in deregulated power system using artificial inteligence techniques

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