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dc.contributor.authorKodogiannis, Vassilis S.*
dc.date.accessioned2021-02-11T08:19:35Z
dc.date.available2021-02-11T08:19:35Z
dc.date.issued2019*
dc.date.submitted2019-11-08 11:31:56*
dc.identifier38846*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/41063
dc.description.abstractElectric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.*
dc.languageEnglish*
dc.subjectTA1-2040*
dc.subjectT1-995*
dc.subject.otherlocalization*
dc.subject.otherreactive power optimization*
dc.subject.othermodel predictive control*
dc.subject.otherCNN*
dc.subject.otherlong short term memory (LSTM)*
dc.subject.othermeter allocation*
dc.subject.otherparticle update mode*
dc.subject.othercombined economic emission/environmental dispatch*
dc.subject.otherglass insulator*
dc.subject.otheremission dispatch*
dc.subject.othergenetic algorithm*
dc.subject.othergrid observability*
dc.subject.otherdefect detection*
dc.subject.otherfeature extraction*
dc.subject.otherparameter estimation*
dc.subject.otherincipient cable failure*
dc.subject.otheractive distribution system*
dc.subject.otherboiler load constraints*
dc.subject.othermultivariate time series*
dc.subject.otherparticle swarm optimization*
dc.subject.otherinertia weight*
dc.subject.otherVMD*
dc.subject.otherNOx emissions constraints*
dc.subject.otherspatial features*
dc.subject.otherpenalty factor approach*
dc.subject.otherself-shattering*
dc.subject.otherdifferential evolution algorithm*
dc.subject.othershort term load forecasting (STLF)*
dc.subject.othergenetic algorithm (GA)*
dc.subject.othereconomic load dispatch*
dc.subject.otherleast square support vector machine*
dc.subject.otherCombustion efficiency*
dc.subject.otherelectricity load forecasting*
dc.titleApplications of Computational Intelligence to Power Systems*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03921-761-8*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
oapen.relation.isbn9783039217618*
oapen.relation.isbn9783039217601*
oapen.pages116*
oapen.edition1st*


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