Detecting and Interpreting Faults in Vulnerable Power Grids with Machine Learning

Odin Foldvik Eikeland, Inga Setsa Holmstrand, Sigurd Bakkejord, Matteo Chiesa, Filippo Maria Bianchi

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageBritish English
Pages (from-to)150686-150699
Number of pages14
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Energy analytics
  • machine learning interpretability
  • power quality disturbances

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