Performance Assessment of Supervised Machine Learning-Based Approaches for Photovoltaic Diagnosis

There has been a notable increase in the adoption of solar energy as a sustainable energy choice in recent years due to its positive environmental impact, and widespread availability. However, factors such as weather, component deterioration, improper installation, and maintenance concerns gradually create conditions in the system that threaten its performance and reliability. This study provides a performance evaluation of different supervised machine learning methods for diagnosing photovoltaic cells. Solar power plant datasets containing environmental and electrical parameters were collected, pre-processed, and used to train various machine learning models, including Decision Tree, Long-Short Term Memory, Multilayer Perceptron, Temporal Convolutional Network, Convolutional Neural Network, Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Random Forest, and Linear Discriminant Analysis algorithms. The models are evaluated using accuracy, precision, recall, f1-score, and confusion matrix metrics. Results demonstrated that the models diagnosed short circuit, abnormal voltage, partial shading, and hotspot faults with varying degrees of accuracy.