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.

Despite the rapid expansion of photovoltaic (PV) installations over the past decade, challenges such as curtailments of renewable energy sources (RESs) and grid constraints continue to limit the capacity of Cyprus’ power system to accommodate higher solar penetration. In this context, grid reliability, defined as the ability to maintain stable operation by balancing supply and demand, minimizing curtailment, and reducing stress on the island network, has emerged as a critical concern. The deployment of PV-plus-storage systems offers a viable solution to enhance grid reliability while alleviating operational constraints. This paper presents a real-world case study of the first commercially deployed grid-connected PV-powered, battery-integrated electric vehicle (EV) charging station in Cyprus. Commissioned in May 2025, the system integrates a 60.32 kW_p rooftop PV array, a 100 kW/97 kWh battery energy storage system (BESS), and a 160 kW DC fast charger. A custom cloud-based energy management platform enables real-time monitoring, forecasting, and optimization under a zero-export scheme. High-resolution operational and weather data were collected between 15 May and 30 November 2025. Over this period, the integrated PV-battery system supplied 29% of the site’s total energy demand (self-sufficiency rate of 28.97%) and achieved a self-consumption rate of 98.69%. Such rates would not have been attainable with a pure PV system, given the depot’s evening-concentrated EV charging demand profile, which requires the BESS to time-shift daytime solar generation. The system reduced depot electricity costs by approximately 29%, generating €16,010 in savings and avoiding 26.47 tonnes of carbon dioxide (CO₂) emissions compared to a grid-only baseline. Beyond site-level performance, the system contributed to grid stress reduction by absorbing excess PV generation that would otherwise have been curtailed/wasted. Operational insights indicate minimal temperature-related issues, highlight the importance of automated fault detection and alerting to minimize downtime, and demonstrate how periodic operation strategies can optimize system performance and mitigate curtailment in Cyprus’s isolated grid.

Predictive maintenance is crucial for optimizing the performance of photovoltaic (PV) systems by proactively addressing potential issues before they escalate. Even though many fault diagnosis methods have been proposed, a major challenge remains the lack of accurate predictive maintenance routines for utility-scale PV systems. The scope of this work is to address this fundamental challenge by proposing a data-driven predictive maintenance alerting routine (that leverages digital twin technologies and decision rules) for detecting and predicting PV system faults. The results demonstrated that the developed predictive maintenance routine yielded a sensitivity of 93.9% for PV utility-scale failure detection over a yearly test period. The findings of the predictive maintenance alerting model proved its capability to anticipate faults up to 10 days in advance, achieving a sensitivity of 83.3%. Finally, the predictive maintenance alerting system can be used by operation and maintenance teams for scheduling corrective actions and optimizing field activities.

To ensure the smooth Terawatt (TW) photovoltaic (PV) transition and secure the TWh power generation over the service lifetime, enhanced digitalization, and automation approaches for effective operation and maintenance (O&M) of PV systems are required. This highlights the urgent need for corrective and predictive analytics to detect and predict possible anomalies in PV systems and to effectively manage and optimally schedule the field O&M activities. To fulfill this need, a failure detection algorithm along with a predictive maintenance model, that leverages statistical and machine learning (ML) principles, are proposed to accurately detect failures and to predict potential fault issues before they occur. The developed algorithm and the model were benchmarked using historical field data from a PV power plant in Greece. The obtained results showed the effectiveness of the algorithm for accurate failure detection and the capability of the predictive model for generating predictive maintenance alarms. The integration of corrective and predictive analytics into a monitoring software solution can help PV plant operators to schedule the field O&M activities, by providing valuable fault insights and actionable maintenance recommendations.