Comparison of complex-valued neural network and fuzzy clustering complex-valued neural network for load-flow analysis

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dc.contributor.authorCeylan, Murat
dc.contributor.authorCetinkaya, Nurettin
dc.contributor.authorCeylan, Rahime
dc.contributor.authorOzbay, Yuksel
dc.date.accessioned2020-03-26T17:03:13Z
dc.date.available2020-03-26T17:03:13Z
dc.date.issued2006
dc.departmentSelçuk Üniversitesien_US
dc.description14th Turkish Symposium on Artificial Intelligence and Neural Networks -- JUN 16-17, 2005 -- Izmir, TURKEYen_US
dc.description.abstractNeural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complexvalued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster.en_US
dc.description.sponsorshipIzmir Inst Technol, EE & CE Depts, Turkish Sci & Res Council, Izmir Branch Chamber Elect & Elect Engineersen_US
dc.identifier.endpage99en_US
dc.identifier.isbn3-540-36713-6
dc.identifier.issn0302-9743en_US
dc.identifier.issn1611-3349en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage92en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/20387
dc.identifier.volume3949en_US
dc.identifier.wosWOS:000239585200011en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartofARTIFICIAL INTELLIGENCE AND NEURAL NETWORKSen_US
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleComparison of complex-valued neural network and fuzzy clustering complex-valued neural network for load-flow analysisen_US
dc.typeConference Objecten_US

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