Physics-Guided Machine Learning for Accurate Series and Shunt Resistance Estimation in Photovoltaic Modules

Accurate estimation of the series (R_s) and shunt (R_sh) resistances from photovoltaic (PV) I–V measurements is essential for device characterization and degradation assessment, yet remains highly sensitive to measurement noise, particularly for conventional slope-based extraction methods. This work proposes a machine-learning–assisted framework for robust estimation of R_s and R_sh from noisy I–V curves by combining physics-informed, normalized features with ensemble regression. A comprehensive set of irradiance-normalized environmental, electrical, slope-related, and current-distribution features is extracted from the full I–V curve and used to train an XGBoost regressor. To further enhance robustness, principal component analysis (PCA) is optionally applied to suppress correlated and noise-sensitive feature directions while preserving physically meaningful variance. Model performance is evaluated across multiple noise levels, with robustness and feature relevance examined using leave-one-feature-out ablation. The proposed framework achieves consistently high accuracy for R_s and significantly improves R_sh estimation under moderate-to-high noise conditions. Relative to the no-PCA baseline, PCA reduces relative error by up to 20–25% for R_s and 10–15% for R_sh, with optimal performance obtained by retaining approximately five principal components. Overall, the results demonstrate that resistance estimation is governed by a low-dimensional, noise-robust subspace of physics-informed features, providing a compact and practical alternative to conventional PV parameter extraction methods.