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dc.contributor.authorFazai, R.en
dc.contributor.authorAbodayeh, K.en
dc.contributor.authorMansouri, M.en
dc.contributor.authorTrabelsi, M.en
dc.contributor.authorNounou, H.en
dc.contributor.authorNounou, M.en
dc.contributor.authorGeorghiou, George E.en
dc.creatorFazai, R.en
dc.creatorAbodayeh, K.en
dc.creatorMansouri, M.en
dc.creatorTrabelsi, M.en
dc.creatorNounou, H.en
dc.creatorNounou, M.en
dc.creatorGeorghiou, George E.en
dc.date.accessioned2021-01-26T09:45:34Z
dc.date.available2021-01-26T09:45:34Z
dc.date.issued2019
dc.identifier.issn0038-092X
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/63289
dc.description.abstractIn this paper, we consider a machine learning approach merged with statistical testing hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The developed method makes use of a machine learning based Gaussian process regression (GPR) technique as a modeling framework, while a generalized likelihood ratio test (GLRT) chart is applied to detect PV system faults. The developed GPR-based GLRT approach is assessed using simulated and real PV data through monitoring the key PV system variables (current, voltage, and power). The computation time, missed detection rate (MDR), and false alarm rate (FAR) are computed to evaluate the fault detection performance of the proposed approach.en
dc.language.isoenen
dc.sourceSolar Energyen
dc.source.urihttp://www.sciencedirect.com/science/article/pii/S0038092X19308126
dc.titleMachine learning-based statistical testing hypothesis for fault detection in photovoltaic systemsen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1016/j.solener.2019.08.032
dc.description.volume190
dc.description.startingpage405
dc.description.endingpage413
dc.author.facultyΠολυτεχνική Σχολή / Faculty of Engineering
dc.author.departmentΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeArticleen
dc.source.abbreviationSolar Energyen
dc.contributor.orcidGeorghiou, George E. [0000-0002-5872-5851]
dc.gnosis.orcid0000-0002-5872-5851


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