Federated Convolutional Transformer Network for Privacy-Preserving Photovoltaic Fault Detection in Distributed Solar Power Systems

Solar energy makes a considerable contribution to global power generation, necessitating photovoltaic (PV) fault detection to ensure high yields in solar power systems. In real-world solar installations, operational data are geographically dispersed, heterogeneous, and sensitive, which imposes privacy restrictions. Existing PV fault-detection techniques have relied on centralized training, requiring raw image data from multiple plants to be collected at a single server, which has led to privacy risks, bias from heterogeneous datasets, and scalability issues. To overcome these challenges, this research provides a novel FL-based PV fault-detection model called Federated Convolutional Transformer Network (Fed-CVTNet), which combines Convolutional Neural Network (CNN) and Vision Transformer (ViT) architecture within the FL framework. Initially, the raw images are pre-processed to enhance the input quality, and the Region of Interest (ROI) is identified via a pretrained YOLO model. Then, the proposed Fed-CVTNet facilitates networked learning among many geographically dispersed clients by only sharing model updates using federated averaging (FedAvg). The experimental findings illustrate that the proposed FL model shows substantial quality improvements compared to the customized CNN-ViT models trained on a dataset of 5600 images and the CNN-ViT models that lack federated aggregation. The highest accuracy achieved by the proposed technique is 98.99%; the proposed Fed-CVTNet has better sensitivity (98.15%) and specificity (99.21%), and much lower false positive and false negative rates than its centralized counterparts. The comparative analysis establishes that federated weight aggregation outperforms centralized baseline models by 2.3% and is effective in reducing the data privacy risk.