Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning
Unscheduled 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 th...
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Online Access: | https://hdl.handle.net/11250/2976292 https://doi.org/10.1109/ACCESS.2021.3127042 |
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ftnorce:oai:norceresearch.brage.unit.no:11250/2976292 2023-05-15T15:08:04+02:00 Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning Eikeland, Odin Foldvik Holmstrand, Inga Setså Bakkejord, Sigurd Chiesa, Matteo Bianchi, Filippo Maria 2021 application/pdf https://hdl.handle.net/11250/2976292 https://doi.org/10.1109/ACCESS.2021.3127042 eng eng urn:issn:2169-3536 https://hdl.handle.net/11250/2976292 https://doi.org/10.1109/ACCESS.2021.3127042 cristin:1977817 Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no 2021, Authors CC-BY IEEE Access Journal article Peer reviewed 2021 ftnorce https://doi.org/10.1109/ACCESS.2021.3127042 2022-10-13T05:50:19Z Unscheduled 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 occurrence of a specific fault, which are valuable for the distribution system operators to implement strategies to prevent and mitigate power disturbances. publishedVersion Article in Journal/Newspaper Arctic NORCE vitenarkiv (Norwegian Research Centre) Arctic IEEE Access 9 150686 150699 |
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English |
description |
Unscheduled 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 occurrence of a specific fault, which are valuable for the distribution system operators to implement strategies to prevent and mitigate power disturbances. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Eikeland, Odin Foldvik Holmstrand, Inga Setså Bakkejord, Sigurd Chiesa, Matteo Bianchi, Filippo Maria |
spellingShingle |
Eikeland, Odin Foldvik Holmstrand, Inga Setså Bakkejord, Sigurd Chiesa, Matteo Bianchi, Filippo Maria Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning |
author_facet |
Eikeland, Odin Foldvik Holmstrand, Inga Setså Bakkejord, Sigurd Chiesa, Matteo Bianchi, Filippo Maria |
author_sort |
Eikeland, Odin Foldvik |
title |
Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning |
title_short |
Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning |
title_full |
Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning |
title_fullStr |
Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning |
title_full_unstemmed |
Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning |
title_sort |
detecting and interpreting faults in vulnerable power grids with machine learning |
publishDate |
2021 |
url |
https://hdl.handle.net/11250/2976292 https://doi.org/10.1109/ACCESS.2021.3127042 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
IEEE Access |
op_relation |
urn:issn:2169-3536 https://hdl.handle.net/11250/2976292 https://doi.org/10.1109/ACCESS.2021.3127042 cristin:1977817 |
op_rights |
Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no 2021, Authors |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1109/ACCESS.2021.3127042 |
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IEEE Access |
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9 |
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150686 |
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150699 |
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