Fault detection and diagnosis methods for photovoltaic systems: A review

• Published in: Renewable & sustainable energy reviews Vol. 91; pp. 1 - 17
• Main Authors: Mellit, A., Tina, G.M., Kalogirou, S.A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2018
• Subjects: Fault detection; Fault diagnosis; Monitoring system; Photovoltaic system; Monitoring system; Fault diagnosis; Fault detection; Photovoltaic system
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2018.03.062

Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly. In addition, if some faults persist (e.g. arc fault, ground fault and line-to-line fault) they can lead to risk of fire. Fault detection and diagnosis (FDD) methods are indispensable for the system reliability, operation at high efficiency, and safety of the PV plant. In this paper, the types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA). Special attention is paid to methods that can accurately detect, localise and classify possible faults occurring in a PVA. The advantages and limits of FDD methods in terms of feasibility, complexity, cost-effectiveness and generalisation capability for large-scale integration are highlighted. Based on the reviewed papers, challenges and recommendations for future research direction are also provided. •Different type of faults including affected components, causes and effects are reported.•Fault detection and diagnosis (FDD) methods of PVSs are extensively reviewed.•Advantages and limits of different FDD methods are illustrated and discussed.•A challenging issue in fault detection and diagnosis of PVS are reported.•Recommendations for future research direction in FDD methods are provided.