Intelligent Maintenance Approaches for Improving Photovoltaic System Performance and Reliability

Abstract

Photovoltaic (PV) systems play a pivotal role in the transition to renewable energy worldwide, yet their long-term performance and cost-effectiveness critically depend on robust Operation and Maintenance (O&M) strategies. While corrective and preventive maintenance have seen significant progress, the development of predictive analytics that proactively generate warnings to anticipate underperformance issues and potential failures remains underexplored. This article makes a substantial contribution by providing a comprehensive review of maintenance approaches, including corrective, preventive, predictive, and extraordinary, with a special focus on the integration of predictive analytics for smart maintenance in PV systems. The study evaluates how cutting-edge technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), facilitate real-time monitoring, diagnostics, and automated early warning systems to anticipate underperformance issues and potential failures, thereby enabling proactive maintenance scheduling. By summarizing the capabilities of these intelligent monitoring systems, the article demonstrates how predictive analytics can significantly reduce unexpected downtime, enhance decision-making, and ultimately lower the levelized cost of energy (LCOE) of PV assets. Finally, the article provides recommendations and outlines future directions for the development of standardized frameworks to optimize smart maintenance practices and improve solar asset management, advancing the state-of-the-art in predictive analytics for the PV industry.