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dc.contributor.authorEikeland, Odin Foldvik
dc.contributor.authorHolmstrand, Inga Setsa
dc.contributor.authorBakkejord, Sigurd
dc.contributor.authorChiesa, Matteo
dc.contributor.authorBianchi, Filippo Maria
dc.date.accessioned2021-12-28T11:16:06Z
dc.date.available2021-12-28T11:16:06Z
dc.date.issued2021-11-10
dc.description.abstractUnscheduled power disturbances cause severe consequences both for customers and grid operators. To defend against such events, it is necessary to identify the causes of interruptions in the power distribution network. In this work, we focus on the power grid of a Norwegian community in the Arctic that experiences several faults whose sources are unknown. First, we construct a data set consisting of relevant meteorological data and information about the current power quality logged by power-quality meters. Then, we adopt machine-learning techniques to predict the occurrence of faults. Experimental results show that both linear and non-linear classifiers achieve good classification performance. This indicates that the considered power quality and weather variables explain well the power disturbances. Interpreting the decision process of the classifiers provides valuable insights to understand the main causes of disturbances. Traditional features selection methods can only indicate which are the variables that, on average, mostly explain the fault occurrences in the dataset. Besides providing such a global interpretation, it is also important to identify the specific set of variables that explain each individual fault. To address this challenge, we adopt a recent technique to interpret the decision process of a deep learning model, called Integrated Gradients. The proposed approach allows gaining detailed insights on the occurreen_US
dc.identifier.citationEikeland, Holmstrand, Bakkejord, Chiesa, Bianchi. Detecting and Interpreting Faults in Vulnerable Power Grids with Machine Learning. IEEE Access. 2021;9:150686-150699en_US
dc.identifier.cristinIDFRIDAID 1963627
dc.identifier.doi10.1109/ACCESS.2021.3127042
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/10037/23521
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofEikeland, O.F. (2023). Enhancing Decision-making in the Electric Power Sector with Machine Learning and Optimization. (Doctoral thesis). <a href=https://hdl.handle.net/10037/31514>https://hdl.handle.net/10037/31514</a>.
dc.relation.journalIEEE Access
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleDetecting and Interpreting Faults in Vulnerable Power Grids with Machine Learningen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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