Power systems are becoming increasingly complex because of renewable integration, distributed generation, electric vehicles, and automation. These changes have created new types of disturbances and faults that are difficult to manage using traditional relay based Protection systems. Artificial intelligence provides data driven techniques that can learn pattern, classify faults, detects anomalies and support intelligent decision making I real time. This review summarizes key AI methods such as machine learning, deep learning, fuzzy logic and reinforcement learning, and explains how different researchers have applied them to power system fault detection and anomaly monitoring. The paper also compares the advantages and limitations of each techniques, and cyber security. Finally, future research directions toward adaptive, explainable, and resilient AI driven protection systems are discussed.
Jenisha et al. (Wed,) studied this question.