Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

The reliability of the generating system adequacy is evaluated based on the ability of the system to satisfy the load demand. In this paper, a novel optimization technique named the disparity evolution genetic algorithm (DEGA) is proposed for reliability assessment of power generation. Disparity evo...

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Main Authors:	Kadhem, Athraa Ali, Noor Izzri, Abdul Wahab, Ishak, Aris, Jasronita, Jasni, Abdalla, Ahmed N.
Format:	Article
Language:	English
Published:	MDPI AG 2017
Subjects:	T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/20435/ http://umpir.ump.edu.my/id/eprint/20435/ http://umpir.ump.edu.my/id/eprint/20435/ http://umpir.ump.edu.my/id/eprint/20435/1/Reliability%20assessment%20of%20power%20generation%20systems%20using%20intelligent%20search%20based%20on%20disparity%20theory.pdf

id	ump-20435
recordtype	eprints
spelling	ump-204352018-10-03T07:29:08Z http://umpir.ump.edu.my/id/eprint/20435/ Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory Kadhem, Athraa Ali Noor Izzri, Abdul Wahab Ishak, Aris Jasronita, Jasni Abdalla, Ahmed N. T Technology (General) The reliability of the generating system adequacy is evaluated based on the ability of the system to satisfy the load demand. In this paper, a novel optimization technique named the disparity evolution genetic algorithm (DEGA) is proposed for reliability assessment of power generation. Disparity evolution is used to enhance the performance of the probability of mutation in a genetic algorithm (GA) by incorporating features from the paradigm into the disparity theory. The DEGA is based on metaheuristic searching for the truncated sampling of state-space for the reliability assessment of power generation system adequacy. Two reliability test systems (IEEE-RTS-79 and (IEEE-RTS-96) are used to demonstrate the effectiveness of the proposed algorithm. The simulation result shows the DEGA can generate a larger variety of the individuals in an early stage of the next population generation. It is also able to estimate the reliability indices accurately. MDPI AG 2017 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/20435/1/Reliability%20assessment%20of%20power%20generation%20systems%20using%20intelligent%20search%20based%20on%20disparity%20theory.pdf Kadhem, Athraa Ali and Noor Izzri, Abdul Wahab and Ishak, Aris and Jasronita, Jasni and Abdalla, Ahmed N. (2017) Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory. Energies, 10 (3). pp. 1-13. ISSN 1996-1073 http://dx.doi.org/10.3390/en10030343 doi: 10.3390/en10030343
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Kadhem, Athraa Ali Noor Izzri, Abdul Wahab Ishak, Aris Jasronita, Jasni Abdalla, Ahmed N. Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory
description	The reliability of the generating system adequacy is evaluated based on the ability of the system to satisfy the load demand. In this paper, a novel optimization technique named the disparity evolution genetic algorithm (DEGA) is proposed for reliability assessment of power generation. Disparity evolution is used to enhance the performance of the probability of mutation in a genetic algorithm (GA) by incorporating features from the paradigm into the disparity theory. The DEGA is based on metaheuristic searching for the truncated sampling of state-space for the reliability assessment of power generation system adequacy. Two reliability test systems (IEEE-RTS-79 and (IEEE-RTS-96) are used to demonstrate the effectiveness of the proposed algorithm. The simulation result shows the DEGA can generate a larger variety of the individuals in an early stage of the next population generation. It is also able to estimate the reliability indices accurately.
format	Article
author	Kadhem, Athraa Ali Noor Izzri, Abdul Wahab Ishak, Aris Jasronita, Jasni Abdalla, Ahmed N.
author_facet	Kadhem, Athraa Ali Noor Izzri, Abdul Wahab Ishak, Aris Jasronita, Jasni Abdalla, Ahmed N.
author_sort	Kadhem, Athraa Ali
title	Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory
title_short	Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory
title_full	Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory
title_fullStr	Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory
title_full_unstemmed	Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory
title_sort	reliability assessment of power generation systems using intelligent search based on disparity theory
publisher	MDPI AG
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/20435/ http://umpir.ump.edu.my/id/eprint/20435/ http://umpir.ump.edu.my/id/eprint/20435/ http://umpir.ump.edu.my/id/eprint/20435/1/Reliability%20assessment%20of%20power%20generation%20systems%20using%20intelligent%20search%20based%20on%20disparity%20theory.pdf
first_indexed	2023-09-18T22:29:27Z
last_indexed	2023-09-18T22:29:27Z
_version_	1777416187582349312

Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

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