Time Series Anomaly Detection for Smart Grids: A Survey

• URL: http://arxiv.org/abs/2107.08835v1
• Date: Fri, 16 Jul 2021 14:33:59 GMT
• Title: Time Series Anomaly Detection for Smart Grids: A Survey
• Authors: Jiuqi (Elise) Zhang, Di Wu, Benoit Boulet
• Abstract summary: anomalous behaviors might be induced by unusual consumption patterns of the users, faulty grid infrastructures, outages, external cyberattacks, or energy fraud. Various methods have been proposed for anomaly detection on power grid time-series data. This paper outlines current research challenges in the power grid anomaly detection domain and reviews the major anomaly detection approaches.
• Score: 2.3853342967891074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid increase in the integration of renewable energy generation and the wide adoption of various electric appliances, power grids are now faced with more and more challenges. One prominent challenge is to implement efficient anomaly detection for different types of anomalous behaviors within power grids. These anomalous behaviors might be induced by unusual consumption patterns of the users, faulty grid infrastructures, outages, external cyberattacks, or energy fraud. Identifying such anomalies is of critical importance for the reliable and efficient operation of modern power grids. Various methods have been proposed for anomaly detection on power grid time-series data. This paper presents a short survey of the recent advances in anomaly detection for power grid time-series data. Specifically, we first outline current research challenges in the power grid anomaly detection domain and further review the major anomaly detection approaches. Finally, we conclude the survey by identifying the potential directions for future research.

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