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dc.contributor.authorTyvold, Torfinn Skarvatun
dc.contributor.authorTorsæter, Bendik Nybakk
dc.contributor.authorAndresen, Christian Andre
dc.contributor.authorHoffmann, Volker
dc.date.accessioned2021-03-08T12:57:05Z
dc.date.available2021-03-08T12:57:05Z
dc.date.created2020-09-24T08:56:40Z
dc.date.issued2020
dc.identifier.isbn978-1-7281-4701-7
dc.identifier.urihttps://hdl.handle.net/11250/2732189
dc.description.abstractIs it possible to reliably predict voltage anomalies in the power grid minutes in advance using machine learning models trained on large quantities of historical data collected by power quality analysers (PQA)? Very little previous research has been done on this topic. To investigate whether this is possible a machine learning model was developed that attempts to predict voltage anomalies 10 minutes in advance based on the presence of early warning signs in the preceding 50 minutes. The model was trained on voltage data collected from 49 measuring locations in the Norwegian power grid. Although results were inconclusive, it was observed that the time that has passed since the previous fault at the same location is a major factor to consider when estimating the probability that a new fault is imminent. It was observed that the probability that a new fault is imminent is proportional to the logarithm of the time passed since the previous anomaly. This means that the risk of a new anomaly is drastically reduced as more time passes since the previous anomaly. This is important to take into consideration when attempting to develop a model that estimates the probability that a new fault is imminent.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartof2020 International Conference on Smart Energy Systems and Technologies - SEST
dc.titleImpact of the Temporal Distribution of Faults on Prediction of Voltage Anomalies in the Power Griden_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.identifier.doihttp://dx.doi.org/10.1109/SEST48500.2020.9203569
dc.identifier.cristin1832820
dc.relation.projectNorges forskningsråd: 268193en_US
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.ispublishedtrue
cristin.fulltextpostprint
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