Automated Fault Detection on Photovoltaic Modules from Aerial Images using Vision Intelligence System
Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Operation and maintenance of photovoltaic (PV) modules are currently the prime concerns of the expanding photovoltaic industry. Aerial inspection of solar modules is becoming
increasingly popular in automatizing operations and maintenance in large-scale photovoltaic power plants. Current practices are typically time-consuming as they make use of manual acquisitions and analysis of thousands of images to scan for faults and anomalies in the modules. Technological advancements and innovative techniques in the fast moving world expect instantaneous results. Fault diagnosis is one such technique that provides instantaneous results and assures enhanced lifetime of various critical components. In this project, we propose solarAI, an artificial intelligence system based on deep learning for anomaly cells detection in photovoltaic images obtained from unmanned aerial vehicles equipped with a thermal
infrared sensor.
