Machine Learning for Photovoltaic Partial-Shading Diagnosis from Current-Voltage Curves

Partial shading deforms photovoltaic (PV) current–voltage (I-V) characteristics and complicates reliable condition monitoring. This study develops and stress-tests a compact, feature-based machine-learning framework using a public model-generated I-V dataset. The primary cohort contains 186 curves at 1000 W m−2, balanced across uniform operation and five nominal shading severities (10%, 20%, 30%, 40%, and 50%) at 31 temperatures from 20 to 50 °C. A verified set of 37 electrical, normalized, maximum-power-point, and curve-shape predictors was evaluated with six classifiers under 30 repeated stratified temperature-grouped five-fold validations, followed by nested grouped confirmation. The selected 10-feature logistic-regression model achieved 1.000 accuracy, balanced accuracy, macro-F1, Matthews correlation coefficient, Cohen’s κ, and ROC-AUC for binary and six-class diagnosis; the temperature-group bootstrap 95% confidence interval for macro-F1 was 1.000–1.000. A single normalized maximum-power-point-voltage predictor also achieved macro-F1 = 1.000, showing that the controlled source domain is strongly and partly rule-separable. The clean high-C solution was highly sensitive to measurement disturbance: under a 5% combined bias, drift, Gaussian, and quantization stress test, mean macro-F1 fell to 0.395 for binary detection and 0.095 for six-class diagnosis, whereas noise-aware training remained 0.976 and 0.962, respectively. Normalized maximum-power-point voltage, fill factor, maximum-power-point voltage, and maximum-power-point power were the dominant features. A uniform-operation negative control across 500–1000 W m−2 also exposed irradiance-domain sensitivity in the noise-aware model. The results establish reproducible controlled-domain discrimination and uncertainty sensitivity, but external measured-data validation across irradiance, modules, array configurations, and shadow geometries remains necessary before deployment.