Effect of Lightning Features on Predicting Outages Related to Thunderstorms in Distribution Grids

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dc.contributor.authorKezunovic, Mladen
dc.contributor.authorBaembitov, Rashid
dc.contributor.authorSaranovic, Daniel
dc.contributor.authorKarmacharya , Abinash
dc.contributor.authorObradovic, Zoran
dc.date.accessioned2024-12-26T21:06:50Z
dc.date.available2024-12-26T21:06:50Z
dc.date.issued2025-01-07
dc.description.abstractPower outages in the distribution grid profoundly impact everyday human activity and economic welfare, as numerous infrastructures rely on uninterrupted power for sustained operation. Over the past decade, there has been a significant focus on using machine learning (ML) to predict outage state of risk (SoR) in both research and applications. One of the main causes of outages is weather conditions causing equipment failure due to wear and tear, as well as lightning strikes, in this paper. Therefore, we analyzed the consequences of selecting various weather-related ML model features on the outage SoR. We first show the outage SoR prediction results with and without considering lightning features, and then rank the SoR prediction performance based on various other weather features.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.360
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.otherc687c797-8a98-4873-9f40-5ebecdc2f4f0
dc.identifier.urihttps://hdl.handle.net/10125/109202
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMonitoring, Control, and Protection
dc.subjectlightning, machine learning, outage prediction, state of risk, weather
dc.titleEffect of Lightning Features on Predicting Outages Related to Thunderstorms in Distribution Grids
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage2974

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