Anomaly Detection in Power Markets and Systems

TL;DR

This paper reviews the increasing importance of anomaly detection in power systems, emphasizing cyber-physical threats, and discusses AI-based methods for identifying issues from consumers to producers to enhance grid security and reliability.

Contribution

It provides a comprehensive overview and classification of common power system anomalies and surveys AI techniques used for anomaly detection in power markets and systems.

Findings

Identification of common power system anomalies

Classification of anomaly detection methods

Discussion of AI techniques in anomaly detection

Abstract

The widespread use of information and communication technology (ICT) over the course of the last decades has been a primary catalyst behind the digitalization of power systems. Meanwhile, as the utilization rate of the Internet of Things (IoT) continues to rise along with recent advancements in ICT, the need for secure and computationally efficient monitoring of critical infrastructures like the electrical grid and the agents that participate in it is growing. A cyber-physical system, such as the electrical grid, may experience anomalies for a number of different reasons. These may include physical defects, mistakes in measurement and communication, cyberattacks, and other similar occurrences. The goal of this study is to emphasize what the most common incidents are with power systems and to give an overview and classification of the most common ways to find problems, starting with the consumer/prosumer end working up to the primary power producers. In addition, this article aimed to discuss the methods and techniques, such as artificial intelligence (AI) that are used to identify anomalies in the power systems and markets.