Maintenance techniques to increase solar energy production: A review

Abstract

This review explores advanced maintenance techniques aimed at improving solar energy production efficiency. The study analyzes the rapid growth of solar energy and the challenges posed by environmental factors such as soiling, harsh climate conditions and hotspots, which reduce photovoltaic (PV) and concentrated solar power (CSP) system performance. Predictive models for solar energy generation and soiling detection, including artificial intelligence (AI) and machine learning (ML) algorithms and Internet of Things (IoT), are discussed as means for optimizing energy production and reducing maintenance costs. It is also emphasized the role of Unmanned Aerial Vehicles (UAVs) to capture images for fault detection and failure prediction, enhancing maintenance accuracy and minimizing downtime. The study concludes by analyzing the role of these techniques to reduce water consumption in cleaning tasks, as well as solutions to increase the operational lifespan and performance of solar plants such as anti-soiling coatings, robotic cleaning systems and accurate predictive models. • High temperatures and UV radiation accelerate solar panel degradation. • Dust buildup (soiling) reduces solar efficiency; solutions include anti-soiling coatings and robotic cleaners. • UAVs with infrared cameras help quickly identify panel defects. • AI algorithms improve solar panel maintenance by predicting and detecting faults, reducing costs and boosting energy output. • IoT networks enhance drones and sensor performance by detecting faults in real time.