A Review of Fault Detection Techniques and performance analysis in Photovoltaic Systems Using IoT

Photovoltaic (PV) systems play an important role in renewable energy generation because of their clean and sustainable nature. Various methods based on electrical parameters such as voltage, current, and Temperature are analyzed to identify common faults including partial shading, open-circuit, short-circuit, and degradation issues. In addition, the role of infrared thermography in detecting hotspots and thermal abnormalities is discussed as an effective non-invasive diagnostic tool. The review also explores the integration of artificial intelligence and machine learning techniques, including deep learning models, for automated and accurate fault identification. Furthermore, IoT-based monitoring systems are examined for real-time data acquisition and remote fault analysis. A comparative analysis of existing studies highlights the advantages, limitations, and accuracy of different approaches. Finally, key challenges and future research directions are outlined to enhance the efficiency, scalability, and intelligence of PV fault detection systems.