A review of artificial intelligence techniques for anomaly detection in smart grid

Abstract In the era of smart grids (SGs), as more interconnected energy sources and renewable sources are used, it is becoming increasingly important to have robust and accurate advanced anomaly detection methods. Due to the complexity of modern power systems, anomalies need to be detected more efficiently. This study provides a comprehensive overview of integrating renewable energy sources into SGs and the increasing importance of robust anomaly detection methods in ensuring grid security and reliability. Addressing four key research areas, we explore the current trends in applying machine learning techniques to SG anomaly detection research, identifying anomalies such as electricity theft, cyber-attacks, power system disturbances, and abnormal consumption patterns. We systematically evaluate the utilization of different machine learning models, including supervised, unsupervised, semi-supervised, and reinforcement learning, to detect each anomaly within SG environments. Furthermore, we assess the effectiveness of the anomaly detection algorithms and discuss the potential for further research, emphasizing the need for multidisciplinary collaboration and continuous development to overcome challenges and adapt to evolving grid dynamics and cyber threats. The findings of this study suggest that machine learning significantly contributes to ensuring the resilience and efficiency of SGs in the face of evolving challenges.