Development of machine learning algorithms for fault detection in power systems - a review

Abstract

Detecting and locating faults within electrical grids presents substantial challenges in power systems engineering, leading to energy loss, reduced revenue, and equipment damage. This paper provides a comprehensive review of the integration and utilization of machine learning algorithms to enhance fault identification processes. Acknowledging the constraints of traditional methods, the paper delves into the historical evolution of fault detection in power systems. By highlighting the significance of machine learning, this review underscores its pivotal role in fault prevention, energy conservation, and bolstering the resilience of power infrastructures.