Genetic algorithms to determine the critical values of a power energy system for different operating conditions

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dc.contributor.authorKandara, Osman
dc.contributor.authorÖztürk, Ali
dc.date.accessioned2020-05-01T12:10:11Z
dc.date.available2020-05-01T12:10:11Z
dc.date.issued2012
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS: 000297087600047en_US
dc.description.abstractCritical values of a power system are the values of load bus active power, load bus voltage amplitude, and load bus angle when the load bus has the highest active power value. These values depend on various factors such as the length, voltage, the number, and the serial and shunt compensation rates of the line. In this study, we take the IEEE's 6-bus power system as our base system. We first used the Newton Raphson (NR) method to determine the critical values under various operating conditions. Then, we used genetic algorithms, which is an artificial intelligence method commonly used in optimization, to determine the critical values under the same operating conditions. At the end, we compared the results. The results show that overall the critical values determined using GA is as good as with or better than those obtained using the traditional NR method.en_US
dc.identifier.endpage982en_US
dc.identifier.issn1308-772X
dc.identifier.issue2en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage971en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/6063
dc.identifier.volume28en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSila Scienceen_US
dc.relation.ispartofEnergy Education Science And Technology Part A-Energy Science And Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGenetic algorithmsen_US
dc.subjectPower system stabilityen_US
dc.subjectPower Distributionen_US
dc.subjectNewton Raphsonen_US
dc.subjectArtificial Intelligenceen_US
dc.titleGenetic algorithms to determine the critical values of a power energy system for different operating conditionsen_US
dc.typeArticleen_US

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