Economic load dispatch using intelligent optimization with fuzzy control

Lai, JCY; Leung, FHF; Ling, SH; Shi, EC

Economic load dispatch using intelligent optimization with fuzzy control

Lai, JCY Leung, FHF Ling, SH

Shi, EC

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2011, pp. 2219 - 2224
Issue Date:: 2011-09-27

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Field	Value	Language
dc.contributor.author	Lai, JCY	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Shi, EC	en_US
dc.date.issued	2011-09-27	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2011, pp. 2219 - 2224	en_US
dc.identifier.isbn	9781424473175	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29558
dc.description.abstract	In this paper, Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm is proposed to tackle the economic load dispatch problem. To provide the self-terminating ability, a technique called Iteration Windows (IW) is introduced to govern the number of iteration in each searching stage during the optimization. The size of IW is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the IW till the end of the searching process. A wavelet based mutation process is embedded in the DE searching process to enhance the searching performance. The weight F of DE is also controlled by the fuzzy controller to further speed up the searching process. The proposed method is employed to solve the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) Problem. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration and performs significantly better than the conventional methods in terms of convergence speed and solution quality. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZY.2011.6007376	en_US
dc.title	Economic load dispatch using intelligent optimization with fuzzy control	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Taipei, Taiwan	en_US
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, Differential Evolution (DE) that incorporates fuzzy control and k-nearest neighbors algorithm is proposed to tackle the economic load dispatch problem. To provide the self-terminating ability, a technique called Iteration Windows (IW) is introduced to govern the number of iteration in each searching stage during the optimization. The size of IW is controlled by a fuzzy controller, which uses the information provided by the k-nearest neighbors system to analyze the population during the searching process. The controller keeps controlling the IW till the end of the searching process. A wavelet based mutation process is embedded in the DE searching process to enhance the searching performance. The weight F of DE is also controlled by the fuzzy controller to further speed up the searching process. The proposed method is employed to solve the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) Problem. It is shown empirically that the proposed method can terminate the searching process with a reasonable number of iteration and performs significantly better than the conventional methods in terms of convergence speed and solution quality. © 2011 IEEE.

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http://hdl.handle.net/10453/29558