A Fault Detection Scheme Utilizing Convolutional Neural Network for PV Solar Panels with High Accuracy
Mary Pa, M. Nasir Uddin, Mohammad Amin Kazemi
Abstract
Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency and identifying the defects in solar PV systems remains a significant challenge. To enhance the robustness of the solar systems, this paper proposes a trained convolutional neural network (CNN) based fault detection scheme to divide the images of photovoltaic modules into faulty and normal classes. For binary classification, the algorithm classifies the input images of PV cells into two categories (i.e. faulty or normal). To further assess the network's capability, the defective PV cells are organized into shadowy, cracked, or dusty cells, and the model is utilized for multiple classifications. The success rate for the proposed CNN model is 91.1% for binary classification, and 88.6% for multi-classification. The proposed trained CNN model remarkably outperforms the CNN model presented in a previous study which used the same datasets. The proposed CNN-based fault detection model is straightforward, simple and effective and could be applied to the fault detection of solar panels.