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dc.contributor.authorKarim, MAen_NZ
dc.contributor.authorCurrie, Jen_NZ
dc.contributor.authorLie, TTen_NZ
dc.date.accessioned2019-03-12T02:13:15Z
dc.date.available2019-03-12T02:13:15Z
dc.date.copyright2018-02-01en_NZ
dc.identifier.citationElectric Power Systems Research, 155, 206-215.
dc.identifier.issn0378-7796en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/12351
dc.description.abstractA microgrid operated in stand alone mode is highly vulnerable to instability when the integration of intermittent energy sources are considered. If a short circuit fault occurs in a microgrid while operating at its design limit, often cost effective system recovery becomes a challenging task. Under such contingencies predictive analysis can be used to strengthen the system restoration schemes. In this study, a system based on machine learning algorithm is implemented to forecast the security of a standalone microgrid and based on the forecasting, schedule multiple backup diesel generators under the contingency of loss of a major generating unit. The underlying objective is to maintain the voltage stability with an optimized economic dispatch scheme, right after clearing a critical three phase short circuit fault. Finally, a promising set of outcomes are observed and discussed.en_NZ
dc.publisherElsevier
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0378779617304212?via%3Dihub
dc.rightsCopyright © 2018 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.subjectMonte Carlo simulation; Distributed generation; Hybrid microgrid; Genetic algorithm; Machine learning
dc.titleA Machine Learning Based Optimized Energy Dispatching Scheme for Restoring a Hybrid Microgriden_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1016/j.epsr.2017.10.015en_NZ
aut.relation.endpage215
aut.relation.startpage206
aut.relation.volume155en_NZ
pubs.elements-id316960
aut.relation.journalElectric Power Systems Researchen_NZ


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