Voting based ensemble for detecting visual faults in photovoltaic modules using AlexNet features

This study proposes a novel approach utilizing a voting-based ensemble technique to diagnose visible faults in photovoltaic (PV) modules from aerial images captured by unmanned aerial vehicles (UAVs), leveraging AlexNet features. The proposed method focuses on classifying commonly occurring visual faults such as glass breakage, snail trails, burn marks, delamination and discoloration. Two voting-based ensemble models, a two-class ensemble (combining support vector machines and k-nearest neighbor) and a three-class ensemble (integrating support vector machines, J48, and k-nearest neighbor) were developed and evaluated against individual machine learning classifiers. Results indicate that the two-class ensemble outperforms the three-class ensemble and other individual classifiers, achieving an accuracy of 98.30%. This approach not only enhances fault diagnosis accuracy but also reduces inspection costs and instrument monitoring efforts contributing to the sustainable and efficient operation of PV systems.