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dc.contributor.authorSepehry, Mojtaba
dc.contributor.authorHeidari-Kapourchali, Mohammad
dc.contributor.authorAravinthan, Visvakumar
dc.date.accessioned2017-06-18T00:22:34Z
dc.date.available2017-06-18T00:22:34Z
dc.date.issued2016
dc.identifier.citationM. Sepehry, M. Heidari-Kapourchali and V. Aravinthan, "Modeling of uncertainty in distribution network reconfiguration using Gaussian Quadrature based approximation method," 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, 2016, pp. 1-5en_US
dc.identifier.isbn978-1-5090-4168-8
dc.identifier.issn1944-9925
dc.identifier.otherWOS:000399937901151
dc.identifier.urihttp://dx.doi.org/10.1109/PESGM.2016.7741509
dc.identifier.urihttp://hdl.handle.net/10057/13357
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractDistribution feeder reconfiguration is a cost-effective scheme to improve the system operating condition. Making decisions on the status of switches along the feeder with high penetration of renewable resources requires a tractable and accurate input data analysis. This paper presents a method using Gaussian quadrature to approximate probability distribution function of input variables with a probability mass function. The method is applied to Weibull wind speed and Beta solar irradiance probability distribution functions. These approximated input variables are fed into an energy loss minimization problem to find the best configuration of 33-bus Baran test system in a typical summer day. Group search optimizer, a swarm intelligence optimization method, is applied to solve the problem. In order to analyze the accuracy and efficiency of the proposed method, Monte Carlo simulation and two other approximation methods-bracket midpoint and bracket mean-are used for comparison. Results show the superiority of the method in computational time and its adequate precision.en_US
dc.description.sponsorshipPower Systems Engineering Research Center (PSerc). Project No: T-53en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2016 Power and Energy Society General Meeting (PESGM);
dc.subjectDiscretizationen_US
dc.subjectReconfigurationen_US
dc.subjectUncertaintyen_US
dc.subjectGaussian quadratureen_US
dc.titleModeling of uncertainty in distribution network reconfiguration using Gaussian Quadrature based approximation methoden_US
dc.typeConference paperen_US
dc.rights.holderCopyright © 2016, IEEEen_US


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