Power Transformer Prognostics and Health Management Using Machine Learning: A Review and Future Directions - Ryad Zemouri (Machines)
Power transformers (PTs) play a vital role in the electrical power system. Assessing their health to predict their remaining useful life is essential to optimise maintenance. Scheduling the right maintenance for the right equipment at the right time is the ultimate goal of any power system utility. Optimal maintenance has a number of benefits: human and social, by limiting sudden service interruptions, and economic, due to the direct and indirect costs of unscheduled downtime. PT now produces large amounts of easily accessible data due to the increasing use of IoT, sensors, and connectivity between physical assets. As a result, power transformer prognostics and health management (PT-PHM) methods are increasingly moving towards artificial intelligence (AI) techniques, with several hundreds of scientific papers published on the topic of PT-PHM using AI techniques. On the other hand, the world of AI is undergoing a new evolution towards a third generation of AI models: large-scale foundation models. What is the current state of research in PT-PHM? What are the trends and challenges in AI and where do we need to go for power transformer prognostics and health management? This paper provides a comprehensive review of the state of the art in PT-PHM by analysing more than 200 papers, mostly published in scientific journals. Some elements to guide PT-PHM research are given at the end of the document.