Application of machine learning in power grid fault detection and maintenance

Abstract The power grid infrastructure serves as the backbone of modern society, providing essential electricity supply to meet the demands of various sectors. Ensuring a reliable and efficient power grid amidst increasing demand remains paramount. This paper provides a literature assessment of the United Kingdom’s (UK) power grid, with a focus on fault occurrences, maintenance techniques, and the use of new technology for monitoring and maintenance. According to the research, insulation degradation is the most common source of power grid problems. The power grid’s maintenance cycle is then investigated, including preventive, predictive, and corrective maintenance techniques. The study emphasises the significance of regular inspections, condition-based monitoring, and asset management strategies in improving grid dependability and longevity. The paper then addresses the concept of Integrated Vehicle Health Management (IVHM) and how it relates to power grid infrastructure. It studies the role of IVHM systems in real-time monitoring, diagnostics, and prognostics for grid assets, allowing for predictive maintenance and informed decision-making. Furthermore, the article studies the use of machine learning approaches to power grid health monitoring and maintenance. This article discusses machine learning methodologies such as supervised and unsupervised learning, as well as reinforcement learning, and how they are used in defect detection, classification, and predictive maintenance. Overall, this paper provides an overview of the UK power grid, its fault management strategies, maintenance cycles, and the integration of machine learning techniques for health monitoring and maintenance, offering insights into enhancing grid reliability and performance in the face of evolving challenges.