Performance Evaluation of Hybrid CNN-SVM model for Efficient Detection of Cracks and Faults in Solar Panels

Maintaining solar panel efficiency has become increasingly critical as the demand for renewable energy grows. For solar panels to operate at their best and save operating expenses, cracks and other defects must be found early. In order to improve the identification of fractures and other defects in solar panels, this study presents a hybrid model that blends Convolutional Neural Networks (CNN) with Support Vector Machines (SVM). While the SVM classifier offers more accurate fault diagnosis, the CNN collects detailed features from panel images, identifying subtle patterns that may signal faults. By automating the monitoring process, our hybrid CNN-SVM approach makes it possible to accurately discover anomalies that are concealed as well as those that are evident. This type facilitates solar panel maintenance by enabling prompt defect identification, which lowers downtime and repair expenses while prolonging the panels’ useful life. This strategy provides a dependable, expandable way to improve solar energy systems’ sustainability and efficiency.