Advanced analytics on IV curves and electroluminescence images of photovoltaic modules using machine learning algorithms

Abstract Advanced analysis and monitoring of photovoltaic solar modules is required to maintain the reliable operations of photovoltaic plants. Hence, it requires diagnostics through current–voltage (IV) curves, electroluminescence (EL) imaging, and other measurement techniques. The analysis through IV characterization provides the discerning insight about the quantitative measure of solar module performance, while the image characterization methods on EL images can capture spatial defects with microscopic resolution such as microcracks, broken cells interconnections, shunts, among many other defect types. The fusion of these two methods with supervised and unsupervised machine learning can generate unique insight with classification, regression, and dimension reductions on IV–EL data. In this study, we have performed the IV–EL correlation by classifying the IV data based on EL image annotation (where the class information is coming from EL image). The feature vectors consist of IV curve parameters and statistical features. We have first applied the unsupervised learning algorithms t ‐distributed stochastic neighbor embedding ( t ‐SNE) and uniform manifold approximation and projection (UMAP) for dimensionality reduction to understand the importance of various features on EL defect types. Furthermore, we had applied feature selection algorithms before applying the classification algorithms. We have performed the classification of various defect types by applying the random forests (RF) and XGBoost algorithm while identifying the top features. The accuracy was achieved greater than 91% and 95%, respectively, for supervised methods on the top five features. This correlation of IV–EL measurement could benefit in quick identification of various defect types in PV modules with only IV curve parameters, given the classification models are modeled using large‐scale datasets and tuned optimally.