  Solar energy is one of the most dependable renewable energy technologies, as
it is feasible almost everywhere globally. However, improving the efficiency of
a solar PV system remains a significant challenge. To enhance the robustness of
the solar system, this paper proposes a trained convolutional neural network
(CNN) based fault detection scheme to divide the images of photovoltaic
modules. 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. Thus, 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 in the
fault detection of solar panel.
