A Hybrid Deep Feature Fusion and CWINCA-Based Classification Framework for Thermal Fault Diagnosis in Photovoltaic Panels

Abstract

Accurate and timely identification of faults in photovoltaic (PV) panels is critical for maintaining system efficiency and ensuring safe operation. In this study, a hybrid classification framework is proposed that integrates deep feature fusion with an advanced feature selection method to detect PV panel faults using thermal infrared imagery. Feature representations were extracted using four pre-trained lightweight convolutional neural networks: MobileNet, MobileNetV2, MobileNetV3Small, and MobileNetV3Large resulting in a 3840-dimensional concatenated feature vector. To reduce redundancy and improve discriminative power, the Cumulative Weight-based Iterative Neighborhood Component Analysis (CWINCA) was employed, selecting 142 informative features. These were subsequently classified using a linear Support Vector Machine (SVM). Experiments were conducted on the publicly available PVF-10 dataset, comprising 5,579 thermal images across ten fault categories. The proposed method achieved an overall classification accuracy of 86.49%, outperforming several individual CNN based architectures. The results demonstrate that combining feature-level integration with targeted selection significantly enhances classification performance while maintaining low computational complexity. This framework offers a promising and scalable solution for UAV-based PV inspection systems.