Time Series Anomaly Detection for Smart Grids: A Survey

TL;DR

This survey reviews recent advances in anomaly detection methods for power grid time-series data, highlighting challenges, approaches, and future research directions to improve grid reliability and security.

Contribution

It provides a comprehensive overview of current anomaly detection techniques in power grids and discusses future research challenges and opportunities.

Findings

Various anomaly detection methods have been proposed for power grid data.

Challenges include handling diverse anomaly types and data complexity.

Future research should focus on integrating advanced machine learning techniques.

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.