Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorSalazar, H.
dc.creatorGallego, R.
dc.creatorRomero, R.
dc.date2014-05-20T13:28:55Z
dc.date2016-10-25T16:48:24Z
dc.date2014-05-20T13:28:55Z
dc.date2016-10-25T16:48:24Z
dc.date2006-07-01
dc.date.accessioned2017-04-05T20:12:22Z
dc.date.available2017-04-05T20:12:22Z
dc.identifierIEEE Transactions on Power Delivery. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc., v. 21, n. 3, p. 1735-1742, 2006.
dc.identifier0885-8977
dc.identifierhttp://hdl.handle.net/11449/9656
dc.identifierhttp://acervodigital.unesp.br/handle/11449/9656
dc.identifier10.1109/TPWRD.2006.875854
dc.identifierWOS:000238704500091
dc.identifierhttp://dx.doi.org/10.1109/TPWRD.2006.875854
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/857748
dc.descriptionOne objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relationIEEE Transactions on Power Delivery
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial neural networks (ANNs)
dc.subjectclustering techniques
dc.subjectfeeder reconfiguration
dc.subjectoptimization techniques
dc.titleArtificial neural networks and clustering techniques applied in the reconfiguration of distribution systems
dc.typeOtro


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