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Optimal power flow solutions for power system operations using moth-flame optimization algorithm

This article proposes a recent novel metaheuristic optimization technique: Moth-Flame Optimizer (MFO) to solve one of the most important problems in the power system namely Optimal power flow (OPF). Three objective functions will be solved simultaneously: minimizing fuel cost, transmission loss, and...

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Main Authors:	Alabd, Salman, Mohd Herwan, Sulaiman, Muhammad Ikram, Mohd Rashid
Format:	Conference or Workshop Item
Language:	English
Published:	Springer Singapore 2020
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/31645/1/Optimal%20Power%20Flow%20Solutions%20for%20Power%20System%20Operations%20Using%20Moth-Flame%20Optimization%20Algorithm.pdf http://umpir.ump.edu.my/id/eprint/31645/ https://link.springer.com/chapter/10.1007/978-981-15-5281-6_15 https://doi.org/10.1007/978-981-15- 5281-6_15
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id	my.ump.umpir.31645
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spelling	my.ump.umpir.316452021-07-27T03:20:51Z http://umpir.ump.edu.my/id/eprint/31645/ Optimal power flow solutions for power system operations using moth-flame optimization algorithm Alabd, Salman Mohd Herwan, Sulaiman Muhammad Ikram, Mohd Rashid TK Electrical engineering. Electronics Nuclear engineering This article proposes a recent novel metaheuristic optimization technique: Moth-Flame Optimizer (MFO) to solve one of the most important problems in the power system namely Optimal power flow (OPF). Three objective functions will be solved simultaneously: minimizing fuel cost, transmission loss, and voltage deviation minimization using a weighted factor. To show the effectiveness of proposed MFO in solving the mentioned problem, the IEEE 30-bus test system will be used. Then the obtained result from the MFO algorithm is compared with other selected well-known algorithms. The comparison proves that MFO gives better results compared to the other compared algorithms. MFO gives a reduction of 14.50% compared to 13.38 and 14.15% for artificial bee colony (ABC) and Improved Grey Wolf Optimizer (IGWO) respectively Springer Singapore 2020-07-08 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31645/1/Optimal%20Power%20Flow%20Solutions%20for%20Power%20System%20Operations%20Using%20Moth-Flame%20Optimization%20Algorithm.pdf Alabd, Salman and Mohd Herwan, Sulaiman and Muhammad Ikram, Mohd Rashid (2020) Optimal power flow solutions for power system operations using moth-flame optimization algorithm. In: 11th National Technical Seminar on Unmanned System Technology 2019 (NUSYS’19), 2 - 3 December 2019 , UMP Gambang, Pahang. pp. 207-219., 666. ISSN 978-981-15-5280-9 ISBN 978-981-15-5281-6 https://link.springer.com/chapter/10.1007/978-981-15-5281-6_15 https://doi.org/10.1007/978-981-15- 5281-6_15
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Alabd, Salman Mohd Herwan, Sulaiman Muhammad Ikram, Mohd Rashid Optimal power flow solutions for power system operations using moth-flame optimization algorithm
description	This article proposes a recent novel metaheuristic optimization technique: Moth-Flame Optimizer (MFO) to solve one of the most important problems in the power system namely Optimal power flow (OPF). Three objective functions will be solved simultaneously: minimizing fuel cost, transmission loss, and voltage deviation minimization using a weighted factor. To show the effectiveness of proposed MFO in solving the mentioned problem, the IEEE 30-bus test system will be used. Then the obtained result from the MFO algorithm is compared with other selected well-known algorithms. The comparison proves that MFO gives better results compared to the other compared algorithms. MFO gives a reduction of 14.50% compared to 13.38 and 14.15% for artificial bee colony (ABC) and Improved Grey Wolf Optimizer (IGWO) respectively
format	Conference or Workshop Item
author	Alabd, Salman Mohd Herwan, Sulaiman Muhammad Ikram, Mohd Rashid
author_facet	Alabd, Salman Mohd Herwan, Sulaiman Muhammad Ikram, Mohd Rashid
author_sort	Alabd, Salman
title	Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_short	Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_full	Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_fullStr	Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_full_unstemmed	Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_sort	optimal power flow solutions for power system operations using moth-flame optimization algorithm
publisher	Springer Singapore
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/31645/1/Optimal%20Power%20Flow%20Solutions%20for%20Power%20System%20Operations%20Using%20Moth-Flame%20Optimization%20Algorithm.pdf http://umpir.ump.edu.my/id/eprint/31645/ https://link.springer.com/chapter/10.1007/978-981-15-5281-6_15 https://doi.org/10.1007/978-981-15- 5281-6_15
_version_	1822922098809503744
score	13.244745

Optimal power flow solutions for power system operations using moth-flame optimization algorithm

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