In recent years, photovoltaic energy has gained global attention due to the advantages of photovoltaic (PV) systems and the abundance of solar energy. Consequently, the installation capacity of PV systems has risen. Despite these benefits and the notable growth of PV systems, they face challenges such as high initial costs, low power conversion efficiency, reliance on environmental conditions, and vulnerability to faults. Fault detection in PV arrays is critical to minimize energy losses and maximize income for users while also improving electricity efficiency and system lifespan. However, because of the non-linear nature of PV systems, it is challenging for protection devices to detect faults, which can lead to safety risks and fire hazards in solar power plants. In this study, a convolutional neural network (CNN), a type of supervised model, was used for fault diagnosis. Yet, like other supervised models, it has several drawbacks: 1) Acquiring labeled PV data is costly and challenging. 2) Updating the trained model is difficult. 3) Visualizing the model is complex. To overcome these limitations, this study introduces a semi-supervised learning model that uses only a small amount of labeled data and normalizes it for better visualization.