Deep learning-based ensemble model for classification of photovoltaic module visual faults

Fault occurrences in photovoltaic (PV) modules can hinder the performance of the system, resulting in reduced lifetime and performance of the modules. PV module (PVM) faults if unmonitored can affect the power transmission through the system, thereby creating short circuits that can be hazardous. Unmanned aerial vehicle (UAV)-based monitoring is one of the most common and widely adopted techniques to detect faults in PVM. Visual images of PVM contain the necessary information about the faults, but sometimes, it becomes difficult even for expert professional to work on large amount of image data. Automatic classification of PVM faults using deep learning techniques can help in providing improved analysis and instantaneous results. The present study adopts renowned deep convolution neural network (CNN) models such as MobileNet V2, Inception V3, and Xception for the classification of PVM. The aforementioned models were trained individually, and the classification performances of the models were observed to be 97.03%, 95.55%, and 92.27%, respectively. A hybrid deep ensemble model is proposed in the study that merges all the aforementioned models. The proposed model produced classification accuracy higher than each of the individual model with a value of 99.04%. Automatic classification using deep ensemble model can help in the accurate identification of faults in PVM from images acquired through UAV. Consequently, this computer-aided and quick diagnosis can eliminate the downtime and fire hazards.