An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem

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dc.contributor.authorBai, Wenlei
dc.contributor.authorEke, İbrahim
dc.contributor.authorLee, Kwang Y.
dc.date.accessioned2020-06-25T18:23:05Z
dc.date.available2020-06-25T18:23:05Z
dc.date.issued2017
dc.departmentKırıkkale Üniversitesi
dc.descriptionEKE, ibrahim/0000-0003-4792-238X
dc.description.abstractThe increasing fuel price has led to high operational cost and therefore, advanced optimal dispatch schemes need to be developed to reduce the operational cost while maintaining the stability of grid. This study applies an improved heuristic approach, the improved Artificial Bee Colony (IABC) to optimal power flow (OPF) problem in electric power grids. Although original ABC has provided robust solutions for a range of problems, such as the university timetabling, training neural networks and optimal distributed generation allocation, its poor exploitation often causes solutions to be trapped in local minima. Therefore, in order to adjust the exploitation and exploration of ABC, the IABC based on the orthogonal learning is proposed. Orthogonal learning is a strategy to predict the best combination of two solution vectors based on limited trials instead of exhaustive trials, and to conduct deep search in the solution space. To assess the proposed method, two fuel cost objective functions with high non-linearity and non-convexity are selected for the OPF problem. The proposed IABC is verified by IEEE-30 and 118 bus test systems. In all case studies, the IABC has shown to consistently achieve a lower cost with smaller deviation over multiple runs than other modern heuristic optimization techniques. For example, the quadratic fuel cost with valve effect found by IABC for 30 bus system is 919.567 $/hour, saving 4.2% of original cost, with 0.666 standard deviation. Therefore, IABC can efficiently generate high quality solutions to nonlinear, nonconvex and mixed integer problems.en_US
dc.description.sponsorshipScientific and Technical Research Council of Turkey (TUBITAK) TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [1059B191300593]; Scientific Research Projects Coordination Unit (BAP) of Kirikkale University (TUBiTAK) [2012/112]en_US
dc.description.sponsorshipI.Eke is supported by The Scientific and Technical Research Council of Turkey (TUBITAK) Turkey, through the postdoctoral research program 2219, under application number 1059B191300593. and Scientific Research Projects Coordination Unit (BAP - project number 2012/112) of Kirikkale University (TUBiTAK).en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1016/j.conengprac.2017.02.010
dc.identifier.endpage172en_US
dc.identifier.issn0967-0661
dc.identifier.issn1873-6939
dc.identifier.scopus2-s2.0-85014224801
dc.identifier.scopusqualityQ1
dc.identifier.startpage163en_US
dc.identifier.urihttps://doi.org/10.1016/j.conengprac.2017.02.010
dc.identifier.urihttps://hdl.handle.net/20.500.12587/7007
dc.identifier.volume61en_US
dc.identifier.wosWOS:000399628600014
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofControl Engineering Practice
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectModern heuristic optimizationen_US
dc.subjectOptimal power flow (OPF)en_US
dc.subjectArtificial bee colony (ABC)en_US
dc.subjectOrthogonal learning (OL)en_US
dc.subjectNonlinear optimizationen_US
dc.titleAn improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problemen_US
dc.typeArticle

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