Abstract

Maintenance is crucial in electrical power systems, as equipment failure leads to financial losses and power outages. Traditional routine maintenance, although essential, is time consuming and expensive. To minimize downtime, there is a need for a proactive approach to identify equipment requiring maintenance. This paper review and proposes the application of machine learning, a subset of artificial intelligence, to automate traditional routine maintenance. By developing a predictive maintenance model, machine learning can help identify potential equipment failures, reducing downtime and associated costs. This review paper highlights the challenges associated with traditional routine maintenance, including high expenses and lengthy downtime. It then presents a machine learning approach as a solution to these challenges. The proposed method leverages machine learning algorithms to analyze equipment data and predict potential failures, enabling proactive maintenance and minimizing downtime. By adopting this approach, electrical power systems can optimize maintenance operations, reduce costs, and ensure reliable power supply. The paper aims to provide a comprehensive overview of the benefits and applications of machine learning in predictive maintenance for electrical power systems.

Key words: Power reliability, Traditional Maintenance strategies, Predictove Maintenance,Power System Infractsructure and Machine Learning.