dc.contributor.author | Høiem, Kristian Wang | |
dc.contributor.author | Santi, Vemund Mehl | |
dc.contributor.author | Torsæter, Bendik Nybakk | |
dc.contributor.author | Langseth, Helge | |
dc.contributor.author | Andresen, Christian Andre | |
dc.contributor.author | Rosenlund, Gjert Hovland | |
dc.date.accessioned | 2020-09-29T06:57:00Z | |
dc.date.available | 2020-09-29T06:57:00Z | |
dc.date.created | 2020-09-24T08:43:08Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-1-7281-4701-7 | |
dc.identifier.uri | https://hdl.handle.net/11250/2680094 | |
dc.description.abstract | There is a growing interest in applying machine learning methods on large amounts of data to solve complex problems, such as prediction of events and disturbances in the power system. This paper is a comparative study of the predictive performance of state-of-the-art supervised machine learning methods. The event prediction models are trained and validated using high-resolution power quality data from measuring instruments in the Norwegian power grid. The recorded event categories in the study were voltage dips, ground faults, rapid voltage changes and interruptions. Out of the tested machine learning methods, the Random Forest models indicated a better prediction performance, with an accuracy of 0.602. The results also indicated that rapid voltage changes (accuracy = 0.710) and voltage dips (accuracy = 0.601) are easiest to predict among the tested power quality events. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | 2020 International Conference on Smart Energy Systems and Technologies - SEST | |
dc.title | Comparative Study of Event Prediction in Power Grids using Supervised Machine Learning Methods | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.identifier.doi | 10.1109/SEST48500.2020.9203025 | |
dc.identifier.cristin | 1832811 | |
dc.description.localcode | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |