Machine Learning based Predictive Maintenance in Distribution Transformers

Abstract

Distribution Transformers are an integral parts of the infrastructure which ensures reliable distribution of electricity in a city across various households. These transformers albeit robust are subject to various internal as well as external factors which may affect it’s working and increase reactive maintenance costs. The increasing complexity and criticality of power systems demand proactive measures for ensuring their reliable operation. Transformer faults represent a significant concern in power infrastructure, leading to downtime, financial losses, and potential safety hazards. This paper explores the application of machine learning techniques for the prediction of transformer faults, aiming to enhance the resilience and efficiency of power distribution systems. The proposed approach leverages advanced machine learning algorithms. By analyzing historical data from transformers, including oil levels, oil temperature, winding temperature, current and voltage levels the model learns patterns indicative of impending faults. The paper emphasizes the development of a predictive maintenance system that can identify potential transformer issues before they escalate, thereby minimizing downtime and reducing maintenance costs. A key component of the project includes model training using historical fault data. The research contributes to the broader field of predictive maintenance in power systems, demonstrating the effectiveness of machine learning in addressing critical infrastructure challenges. The outcomes of this project are expected to offer utilities and power system operators an invaluable tool for improving the reliability and longevity of transformers. Additionally, the integration of machine learning in fault prediction aligns with the industry’s shift towards smart grids and proactive maintenance strategies, contributing to a more sustainable and resilient energy infrastructure.

Topics & Concepts