Automatic fault detection of utility-scale photovoltaic solar generators applying aerial infrared thermography and orthomosaicking

Abstract

As large-scale Photovoltaic (PV) power plants are being expanded in installation number and capacity, aerial infrared thermography (aIRT) has proven to be effective in detecting at different phases of their development, construction and commissioning to operation and maintenance. However, evaluating the aerial imagery over hundreds of hectares fields of PV arrays is very time-consuming and subject to human error. This paper proposes a complete framework for automatically detecting faults in large-scale PV power plants and their physical location inside the plant site. To this end, a Mask-RCNN algorithm is developed and fine-tuned for instance segmentation using a dataset of 93 samples collected in an aIRT flight campaign in Brazil. The results are combined with orthomosaic techniques to create an orthomap of the PV system with the highlighted faults. The proposed method has been tested to automatically detect the faults in two power plants. Several tests were performed to improve the algorithm’s accuracy, resulting in high-accuracy results for detecting and localizing hot spots in PV plants and disconnected substrings. The resulting maps could successfully show the location of these faults with high accuracy (10% of false positives).