Issues and Challenges in Fault Detection for Commercial Solar Panels with Smart AI Image Processing Techniques—A Case Study in an Australian Utility Company

In recent years, renewable energy has become a high-priority choice in utility companies to reduce their carbon emission footprint and cut overall energy costs. As a result, worldwide utility companies are building solar farms to supply electricity for operation usage. Since solar PV panels are relatively new assets for the organization to manage, organizations are continuously seeking insightful understandings to develop and optimize their solar panel maintenance programs. For example, some organizations utilize Unmanned Aerial Vehicle (UAV) technologies combined with an AI-driven image processing technique for condition monitoring to improve the efficiency and safety of the solar maintenance program. While the emerging AI technologies sound promising, this research found that the existing solar panel maintenance programs could still be time-consuming, unsafe, and costly as they might require personnel to visually inspect these assets due to various issues associated with the AI technologies. Therefore, based on a case study, this research investigated the challenges and issues in the existing solar panel maintenance process from technology, organization, and people perspectives. These findings can contribute to a more effective and efficient commercial solar panel maintenance approach.