Photovoltaic (PV) systems must be maintained to ensure their efficiency and longevity. Regularly inspecting and cleaning the panels, wiring, and inverters improves system performance. Monitoring system performance and behavior can help identify possible problems before they become a failure. The PV system should be evaluated on a regular basis for structural stability and physical damage. To prevent system degradation, repairs or replacements must be undertaken as soon as possible. Effective PV system maintenance maximizes energy output and maintains the long-term viability of renewable energy. Incorporating deep learning strategies into PV system maintenance is a promising area of research. In order to detect and diagnose defects in real time, deep learning algorithms can analyze massive amounts of data collected from these systems. This allows for predictive maintenance, where problems are fixed before they have a chance to cause a total system failure. This study aims to analyze the major faults that commonly occur in photovoltaic systems and investigate their influence on various electrical parameters. By understanding the impact of these faults, we aim to identify key features that can be used for deep learning algorithms to enhance fault detection and diagnosis in PV systems.