A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis - Anna Michail; Andreas Livera; Georgios Tziolis; Juan Luis Carús Candás; Alberto Fernandez; Elena Antuña Yudego; Diego Fernández Martínez; Angelos Antonopoulos; Achilleas Tripolitsiotis; Panagiotis Partsinevelos; Eftichis Koutroulis; George E. Georghiou (Heliyon, 2024)
Accurate photovoltaic (PV) diagnosis is of paramount importance for reducing investment risk and increasing the bankability of the PV technology. The application of fault diagnostic solutions and troubleshooting on operating PV power plants is vital for ensuring optimal energy harvesting, increased power generation production and optimised field operation and maintenance (O&M) activities. This study aims to give an overview of the existing approaches for PV plant diagnosis, focusing on unmanned aerial vehicle (UAV)-based approaches, that can support PV plant diagnostics using imaging techniques and data-driven analytics. This review paper initially outlines the different degradation mechanisms, failure modes and patterns that PV systems are subjected and then reports the main diagnostic techniques. Furthermore, the essential equipment and sensor's requirements for diagnosing failures in monitored PV systems using UAV-based approaches are provided. Moreover, the study summarizes the operating conditions and the various failure types that can be detected by such diagnostic approaches. Finally, it provides recommendations and insights on how to develop a fully functional UAV-based diagnostic tool, capable of detecting and classifying accurately failure modes in PV systems, while also locating the exact position of faulty modules.