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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Autores principales:	Odin Foldvik Eikeland, Inga Setsa Holmstrand, Sigurd Bakkejord, Matteo Chiesa, Filippo Maria Bianchi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Energy analytics machine learning interpretability power quality disturbances Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e6cf18fd246c4185a14693093cd6ecef
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id	oai:doaj.org-article:e6cf18fd246c4185a14693093cd6ecef
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spelling	oai:doaj.org-article:e6cf18fd246c4185a14693093cd6ecef2021-11-18T00:08:49ZDetecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning2169-353610.1109/ACCESS.2021.3127042https://doaj.org/article/e6cf18fd246c4185a14693093cd6ecef2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9610059/https://doaj.org/toc/2169-3536Unscheduled 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.Odin Foldvik EikelandInga Setsa HolmstrandSigurd BakkejordMatteo ChiesaFilippo Maria BianchiIEEEarticleEnergy analyticsmachine learning interpretabilitypower quality disturbancesElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150686-150699 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Energy analytics machine learning interpretability power quality disturbances Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Energy analytics machine learning interpretability power quality disturbances Electrical engineering. Electronics. Nuclear engineering TK1-9971 Odin Foldvik Eikeland Inga Setsa Holmstrand Sigurd Bakkejord Matteo Chiesa Filippo Maria Bianchi Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning
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.
format	article
author	Odin Foldvik Eikeland Inga Setsa Holmstrand Sigurd Bakkejord Matteo Chiesa Filippo Maria Bianchi
author_facet	Odin Foldvik Eikeland Inga Setsa Holmstrand Sigurd Bakkejord Matteo Chiesa Filippo Maria Bianchi
author_sort	Odin Foldvik Eikeland
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
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/e6cf18fd246c4185a14693093cd6ecef
work_keys_str_mv	AT odinfoldvikeikeland detectingandinterpretingfaultsinvulnerablepowergridswithmachinelearning AT ingasetsaholmstrand detectingandinterpretingfaultsinvulnerablepowergridswithmachinelearning AT sigurdbakkejord detectingandinterpretingfaultsinvulnerablepowergridswithmachinelearning AT matteochiesa detectingandinterpretingfaultsinvulnerablepowergridswithmachinelearning AT filippomariabianchi detectingandinterpretingfaultsinvulnerablepowergridswithmachinelearning
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Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning

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