AI-Enabled Predictive Maintenance for Distribution Transformers

Abstract

Power distribution networks depend on distribution transformers to work well, which makes sure that there is a steady flow of energy. But these transformers can fail in a number of ways, which can cause expensive downtime and service interruptions. Traditional methods of maintenance, like regular checks and preventative maintenance, aren't always effective and can cost more than they need to. In recent years, there has been a rise in interest in using machine learning (ML) and artificial intelligence (AI) to plan ahead for repair on power transformers. AI-powered predictive maintenance systems can look at both old and new data from transformers to find patterns and trends that could mean they are about to break down or malfunction. It is possible to improve upkeep tasks and lower the risk of unexpected downtime by predicting these problems before they happen. This paper gives a full picture of predicted maintenance for distribution transformers that use AI. It talks about the main problems with standard care methods and shows why using AI-driven methods is better. The study also talks about current AI-based forecast maintenance methods, such as preparing data, choosing features, and training models. In addition, it looks into the possibility of combining IoT devices to collect data and watch things in real time. In addition, the study talks about the problems and restrictions of using AI-powered predictive maintenance systems, like the need to constantly update models and worries about data privacy. The report also looks at the financial and environmental effects of putting these systems in place, focusing on the chances of saving money and making things last longer.