Anomaly Detection Techniques in Smart Grid Systems: A Review

TL;DR

This review paper surveys recent advancements in anomaly detection techniques within smart grid systems, highlighting research challenges, categorizing methods, and suggesting future research directions.

Contribution

It provides a comprehensive categorization and analysis of recent anomaly detection methods in smart grids, identifying research gaps and proposing future directions.

Findings

Various anomaly detection techniques are applied in smart grids.

Research challenges include handling diverse anomaly types and data complexity.

Future research should focus on integrated, real-time detection methods.

Abstract

Smart grid data can be evaluated for anomaly detection in numerous fields, including cyber-security, fault detection, electricity theft, etc. The strange anomalous behaviors may have been caused by various reasons, including peculiar consumption patterns of the consumers, malfunctioning grid infrastructures, outages, external cyber-attacks, or energy fraud. Recently, anomaly detection of the smart grid has attracted a large amount of interest from researchers, and it is widely applied in a number of high-impact fields. One of the most significant challenges within the smart grid is the implementation of efficient anomaly detection for multiple forms of aberrant behaviors. In this paper, we provide a scoping review of research from the recent advancements in anomaly detection in the context of smart grids. We categorize our study from numerous aspects for deep understanding and inspection of the research challenges so far. Finally, after analyzing the gap in the reviewed paper, the direction for future research on anomaly detection in smart-grid systems has been provided briefly.