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Risk based security assessment of power system using generalized regression neural network with feature extraction

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dc.citedby10
dc.contributor.authorMarsadek M.en_US
dc.contributor.authorMohamed A.en_US
dc.contributor.authorid26423183000en_US
dc.contributor.authorid57195440511en_US
dc.date.accessioned2023-12-29T07:44:45Z
dc.date.available2023-12-29T07:44:45Z
dc.date.issued2013
dc.description.abstractA comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy. � 2013 Central South University Press and Springer-Verlag Berlin Heidelberg.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11771-013-1508-9
dc.identifier.epage479
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84890537980
dc.identifier.spage466
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84890537980&doi=10.1007%2fs11771-013-1508-9&partnerID=40&md5=7e00fb5d7a3b422fd5284cfec58173b1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30127
dc.identifier.volume20
dc.pagecount13
dc.sourceScopus
dc.sourcetitleJournal of Central South University
dc.subjectgeneralized regression neural network
dc.subjectline overload
dc.subjectlow voltage
dc.subjectprinciple component analysis
dc.subjectrisk index
dc.subjectvoltage collapse
dc.subjectElectric loads
dc.subjectFeature extraction
dc.subjectMeteorology
dc.subjectNeural networks
dc.subjectPrincipal component analysis
dc.subjectGeneralized regression neural networks
dc.subjectline overload
dc.subjectLow voltages
dc.subjectPrinciple component analysis
dc.subjectRisk indices
dc.subjectVoltage collapse
dc.subjectRisk assessment
dc.titleRisk based security assessment of power system using generalized regression neural network with feature extractionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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