Single and multi-objective optimal power flow using a new differential-based harmony search algorithm

Abbasi, M; Abbasi, E; Mohammadi-Ivatloo, B

Single and multi-objective optimal power flow using a new differential-based harmony search algorithm

Abbasi, M

Abbasi, E Mohammadi-Ivatloo, B

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Journal of Ambient Intelligence and Humanized Computing, 2021, 12, (1), pp. 851-871
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Abbasi, M https://orcid.org/0000-0003-3339-1258
dc.contributor.author	Abbasi, E
dc.contributor.author	Mohammadi-Ivatloo, B
dc.date.accessioned	2021-03-22T13:29:29Z
dc.date.available	2021-03-22T13:29:29Z
dc.date.issued	2021-01-01
dc.identifier.citation	Journal of Ambient Intelligence and Humanized Computing, 2021, 12, (1), pp. 851-871
dc.identifier.issn	1868-5137
dc.identifier.issn	1868-5145
dc.identifier.uri	http://hdl.handle.net/10453/147439
dc.description.abstract	© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. This article proposes a new differential evolutionary-based approach to solve the optimal power flow (OPF) problem in power systems. The proposed approach employs a differential-based harmony search algorithm (DH/best) for optimal settings of OPF control variables. The proposed algorithm benefits from having a more effective initialization method and a better updating procedure in contrast with other algorithms. Here, real power losses minimization, voltage profile improvement, and active power generation minimization are considered as the objectives and formulated in the form of single-objective and multi-objective functions. For proving the performance of the proposed algorithm, comprehensive simulations have been performed by MATLAB software in which IEEE 118-bus and 57-bus systems are considered as the test systems. Besides, thorough comparisons have been performed between the proposed algorithm and other well-known algorithms like PSO, NSGAII, and Harmony search in three different load levels indicating the higher efficiency and robustness of the proposed algorithm in contrast with others.
dc.language	English
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	Journal of Ambient Intelligence and Humanized Computing
dc.relation.isbasedon	10.1007/s12652-020-02089-6
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing
dc.title	Single and multi-objective optimal power flow using a new differential-based harmony search algorithm
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-22T13:29:20Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	1

Abstract:

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. This article proposes a new differential evolutionary-based approach to solve the optimal power flow (OPF) problem in power systems. The proposed approach employs a differential-based harmony search algorithm (DH/best) for optimal settings of OPF control variables. The proposed algorithm benefits from having a more effective initialization method and a better updating procedure in contrast with other algorithms. Here, real power losses minimization, voltage profile improvement, and active power generation minimization are considered as the objectives and formulated in the form of single-objective and multi-objective functions. For proving the performance of the proposed algorithm, comprehensive simulations have been performed by MATLAB software in which IEEE 118-bus and 57-bus systems are considered as the test systems. Besides, thorough comparisons have been performed between the proposed algorithm and other well-known algorithms like PSO, NSGAII, and Harmony search in three different load levels indicating the higher efficiency and robustness of the proposed algorithm in contrast with others.

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http://hdl.handle.net/10453/147439