Abstract 

As photovoltaic (PV) systems become an essential part of the global renewable energy mix, ensuring their long-term performance and operational reliability has become a major priority. Despite significant technological progress, PV installations remain vulnerable to degradation, environmental wear, and equipment failures that may not present immediate, visible signs. Predictive maintenance (PdM) strategies are therefore gaining prominence by integrating real-time sensor data analytics with autonomous robotic systems. These approaches enable continuous condition monitoring of PV components, early anomaly detection, and targeted maintenance interventions. At the core of PdM is the Internet of Things (IoT), where smart sensors installed throughout the PV system collect data on voltage, current, temperature, irradiance, and power output. Although raw sensor data can be noisy and complex due to environmental fluctuations, its value emerges through processing with advanced computational tools. Artificial Intelligence (AI) and Machine Learning (ML) algorithms play a central role, with supervised learning models—such as support vector machines, decision trees, and neural networks—trained to detect specific faults like string disconnections, hotspots, or inverter inefficiencies, while unsupervised learning methods reveal anomalies or rare failure modes even without labeled data. These models not only detect failures but also predict future risks, enabling a transition from reactive or scheduled maintenance to proactive, condition-based strategies. Such predictive insights improve system uptime, lower costs, and extend component lifetimes, with cloud-based platforms further allowing remote, scalable monitoring of multiple PV sites, ranging from small rooftop units to utility-scale farms. However, diagnosis alone is insufficient, especially in remote or large-scale projects, which has driven the integration of robotic systems into PdM workflows. Aerial drones equipped with thermal and optical sensors can autonomously inspect solar arrays, quickly identifying hotspots, shading, soiling, or physical damage, while ground robots handle cleaning tasks, snow removal, and close-up verification of suspected faults. These robotic inspections are faster, safer, and more consistent than manual approaches. Seamless collaboration between sensors, AI-driven analytics, and robotic systems is enabled through IoT and edge computing, where local edge devices process data in real time to support immediate actions, such as dispatching a drone when an abnormal temperature rise is detected. Furthermore, digital twins of PV systems allow simulations of fault scenarios, stress testing of predictive models, and dynamic optimization of maintenance strategies. Despite these advances, several challenges remain, including environmental variability that can introduce misleading patterns, system heterogeneity across hardware and layouts, and the persistent threat of cybersecurity risks due to distributed data transmission. Current research therefore emphasizes hybrid approaches combining physics-based models with AI-driven analytics to improve robustness, as well as privacy-preserving methods such as federated learning to train predictive models across distributed systems. Moreover, efforts are being made toward standardizing data protocols and ensuring interoperability across platforms to enhance scalability. In summary, predictive maintenance through smart sensors, AI analytics, and robotic systems represents a paradigm shift in PV system management, enabling proactive, efficient, and cost-effective operations. By combining intelligence, automation, and connectivity, these strategies maximize energy yield, minimize downtime, and contribute to a more sustainable, resilient renewable energy infrastructure that supports the accelerating global reliance on solar power.

Dr. Dimitris Ziouzios has completed his PhD in 2023 from the University of Western Macedonia (Greece) at the Department of Electrical & Computer Engineering. He is a postdoctoral researcher at the University of Western Macedonia (Greece) at the Department of Chemical Engineering at the field of robotics and Renewable Energy Sources. He has published more than 35 papers in reputed journals and conferences, and has been serving as an editorial board member of repute.