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dc.contributor.authorBretas, Arturo Sumanen_US
dc.date.accessioned2014-03-14T20:19:47Z
dc.date.available2014-03-14T20:19:47Z
dc.date.issued2001-12-04en_US
dc.identifier.otheretd-12052001-101959en_US
dc.identifier.urihttp://hdl.handle.net/10919/29931
dc.description.abstractThis dissertation is a systematic study of artificial neural networks (ANN) applications in power system restoration (PSR). PSR is based on available generation and load to be restored analysis. A literature review showed that the conventional PSR methods, i.e. the pre-established guidelines, the expert systems method, the mathematical programming method and the petri-net method have limitations such as the necessary time to obtain the PSR plan. ANN may help to solve this problem presenting a reliable PSR plan in a smaller time. Based on actual and past experiences, a PSR engine based on ANN was proposed and developed. Data from the Iowa 162 bus power system was used in the implementation of the technique. Reactive and real power balance, fault location, phase angles across breakers and intentional islanding were taken into account in the implementation of the technique. Constraints in PSR as thermal limits of transmission lines (TL), stability issues, number of TL used in the restoration plan and lockout breakers were used to create feasible PSR plans. To compare the time necessary to achieve the PSR plan with another technique a PSR method based on a breadth-search algorithm was implemented. This algorithm was also used to create training and validation patterns for the ANN used in the scheme. An algorithm to determine the switching sequence of the breakers was also implemented. In order to determine the switching sequence of the breakers the algorithm takes into account the most priority loads and the final system configuration generated by the ANN. The PSR technique implemented is composed by several pairs of ANN, each one assigned to an individual island of the system. The restoration of the system is done in parallel in each island. After each island is restored the tie lines are closed. The results encountered shows that ANN based schemes can be used in PSR helping the operators restore the system under the stressful conditions following a blackout.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartChapter0_Complete.pdfen_US
dc.relation.haspartChapters.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectArtificial Neural Networksen_US
dc.subjectPower System Restorationen_US
dc.titleRobust Electric Power Infrastructures. Response and Recovery during Catastrophic Failuresen_US
dc.typeDissertationen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairPhadke, Arun G.en_US
dc.contributor.committeememberVanLandingham, Hugh F.en_US
dc.contributor.committeememberLiu, Yiluen_US
dc.contributor.committeememberDe La Ree Lopez, Jaimeen_US
dc.contributor.committeememberKohler, Werner E.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12052001-101959/en_US
dc.date.sdate2001-12-05en_US
dc.date.rdate2002-12-06
dc.date.adate2001-12-06en_US


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