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dc.contributor.authorOladeji, Ien_NZ
dc.contributor.authorZamora, Ren_NZ
dc.contributor.authorLie, TTen_NZ
dc.date.accessioned2021-10-18T01:51:55Z
dc.date.available2021-10-18T01:51:55Z
dc.identifier.citationEnergies, 14(20), 6639. doi:10.3390/en14206639
dc.identifier.issn1996-1073en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/14583
dc.description.abstractThe proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.en_NZ
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/1996-1073/14/20/6639
dc.rights© 2021 by the authors. Li censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con ditions of the Creative Commons At tribution (CC BY) license (https://cre ativecommons.org/licenses/by/4.0/)
dc.subjectSecurity; Incremental machine learning; Renewable energy sources; Distributed generation
dc.titleAn Online Security Prediction and Control Framework for Modern Power Gridsen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.3390/en14206639en_NZ
aut.relation.endpage6639
aut.relation.issue20en_NZ
aut.relation.startpage6639
aut.relation.volume14en_NZ
pubs.elements-id441522
aut.relation.journalEnergiesen_NZ


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