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dc.contributor.authorKarim, MAen_NZ
dc.contributor.authorCurrie, Jen_NZ
dc.contributor.authorLie, T-Ten_NZ
dc.date.accessioned2020-07-07T01:42:32Z
dc.date.available2020-07-07T01:42:32Z
dc.identifier.citationEnergies 2020, 13(13), 3494; https://doi.org/10.3390/en13133494
dc.identifier.issn1996-1073en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/13507
dc.description.abstractNumerous online methods for post-fault restoration have been tested on different types of systems. Modern power systems are usually operated at design limits and therefore more prone to post-fault instability. However, traditional online methods often struggle to accurately identify events from time series data, as pattern-recognition in a stochastic post-fault dynamic scenario requires fast and accurate fault identification in order to safely restore the system. One of the most prominent methods of pattern-recognition is machine learning. However, machine learning alone is neither sufficient nor accurate enough for making decisions with time series data. This article analyses the application of feature selection to assist a machine learning algorithm to make better decisions in order to restore a multi-machine network which has become islanded due to faults. Within an islanded multi-machine system the number of attributes significantly increases, which makes application of machine learning algorithms even more erroneous. This article contributes by proposing a distributed offline-online architecture. The proposal explores the potential of introducing relevant features from a reduced time series data set, in order to accurately identify dynamic events occurring in different islands simultaneously. The identification of events helps the decision making process more accurate.en_NZ
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/1996-1073/13/13/3494
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.subjectSelf-healing grid; Machine-learning; Feature extraction; Event detection
dc.titleDistributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-healingen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.3390/en13133494en_NZ
aut.relation.endpage3494
aut.relation.issue13en_NZ
aut.relation.startpage3494
aut.relation.volume13en_NZ
pubs.elements-id382616
aut.relation.journalEnergiesen_NZ


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