Machine Learning for Fault Identification in Power Transformers

Abstract

Power transformers play a critical role in the efficient and reliable distribution of electrical energy. Timely detection and diagnosis of faults in transformers are crucial for preventing costly downtime, ensuring safety, and maintaining the integrity of power systems. Traditional methods for fault identification in transformers often rely on manual inspection and periodic testing, which can be time-consuming, labour-intensive, and prone to human error. Machine learning (ML) techniques offer promising solutions for automating fault detection and diagnosis processes in power transformers. In recent years, machine learning (ML) techniques have emerged as promising tools for automating fault detection and diagnosis processes in power transformers. ML algorithms can analyze large volumes of data collected from transformer sensors to identify patterns indicative of various faults, including winding faults, insulation degradation, and overheating. By leveraging ML, utilities and operators can move towards predictive and proactive maintenance strategies, minimizing the risk of catastrophic failures and optimizing asset performance. This paper presents a comprehensive review of recent advancements in applying ML algorithms for fault identification in power transformers. It explores various ML techniques, including supervised and unsupervised learning, reinforcement learning, and deep learning, highlighting their strengths and limitations in transformer fault detection. The paper discusses data availability, model interpretability, and generalization to address these challenges and unlock the full potential of ML in enhancing the reliability and efficiency of power systems.