Exploring the Evolution of Fault Detection Techniques in Renewable Energy Systems: An Analysis of Traditional Methods and Deep Learning Approaches

With the global transition toward renewable energy sources, the timely detection and diagnosis of faults in these energy systems have become increasingly crucial. Solar, wind, hydro, and geothermal power plants, which form the foundation of renewable energy systems, face numerous challenges stemming from environmental, operational, and technical factors that can result in faults and failures. Consequently, implementing robust fault detection and diagnosis mechanisms is paramount in ensuring the reliable protection and continuous operation of digitalized energy systems. Beyond facilitating swift and efficient fault identification, fault diagnosis offers additional benefits such as minimized downtime, reduced maintenance costs, and improved overall system health. However, the escalating complexity and variability inherent in renewable energy systems render traditional analysis and control methods ineffective. To bridge this gap, deep learning (DL) techniques, a groundbreaking avenue within machine learning (ML), have emerged as a promising technology path. DL techniques demonstrate remarkable capabilities in data analysis, prediction, and classification. Accordingly, this chapter presents contemporary approaches to fault detection and diagnosis in digitalized energy systems, leveraging the potential of DL techniques. These innovative solutions aim to effectively address the intricacies presented by renewable energy systems, ensuring their reliable and sustainable operation.