An intelligent flying system for automatic detection of faults in photovoltaic plants

For several years, fault diagnosis of photovoltaic (PV) plants has been manually performed by the human operator by a visual inspection or automatically, by evaluating electrical measures collected by sensors mounted on each PV module. In recent years, a notable interest of the scientific community has been devoted towards the definition of algorithms able to automatically analyse the sequence of images acquired by a thermal camera mounted on board of an unmanned aerial vehicle (UAV) for early PV anomaly detection. In this paper, we define a model-based approach for the detection of the panels, which uses the structural regularity of the PV string and a novel technique for local hot spot detection, based on the use of a fast and effective algorithm for finding local maxima in the PV panel region. Finally, we introduce the concept of global hot spot detection, namely a multi-frame recognition of PV faults which further improves the anomaly detection accuracy of the proposed method. The algorithm has been designed and optimized so as to run in real-time directly on an embedded system on board of the UAV. The accuracy of the proposed approach has been experimented on several video sequences with a standard protocol in terms of Precision, Recall and F-Score, so that our dataset and our quantitative results can be used for future comparisons and to evaluate the reliability of computer vision techniques designed for thermographic PV inspection.