The reliable operation of photovoltaic (PV) systems is essential for ensuring maximum energy yield, system safety, and long-term economic viability. However, PV installations are susceptible to various faults, including open-circuit faults, short-circuit faults, line-to-line faults, ground faults, partial shading, hotspot formation, and degradation-related issues, which can significantly affect system performance and reliability. Effective fault detection techniques are therefore required to identify abnormal operating conditions at an early stage and minimize energy losses.

This paper presents a literature-based performance assessment of fault detection techniques reported in recent photovoltaic system studies. A comprehensive review of published research is conducted to examine conventional, signal-processing-based, and artificial intelligence-driven fault detection approaches. The selected techniques are evaluated using performance indicators reported in the literature, including detection accuracy, response time, computational requirements, implementation complexity, and applicability under different operating conditions. Furthermore, the strengths and limitations of various methods are systematically compared to highlight their suitability for practical PV installations.

The analysis reveals that data-driven and machine learning-based techniques have demonstrated promising fault detection capabilities, particularly in complex and dynamic environments. However, challenges related to data availability, model generalization, real-time implementation, and scalability remain significant concerns. Based on the findings, key research gaps and future development directions are identified to support the advancement of reliable and intelligent fault diagnosis systems for modern photovoltaic applications. The outcomes of this study provide researchers, engineers, and system operators with a consolidated understanding of current fault detection technologies and their performance characteristics in photovoltaic systems.