Early Detection of Anomalies in Photovoltaic Module Strings Using Decision Trees for MPPT Solar Charger Systems - Reddy, D.Ramesh; Saravanan , T. S.; Subhashini, P .; Selvan, Saravana; D'Souza, Sonia Maria; M, Thiyagesan; DuraiRaj, M .; L , Raja-‎sekhara Babu (International Journal of Basic and Applied Sciences, 2025)
This study presents a decision tree (DT) based machine learning approach to detect early anomalies and faults in solar maximum power point ‎tracking (MPPT) integrated photovoltaic (PV) module circuits. A four-panel array, built using a single diode model, is simulated to generate ‎a synthetic, balanced dataset for training and evaluation. Among the different models tested, including neural networks (NN) and support ‎vector classifiers (SVC), the DT model showed the best performance in precision and recall across all anomaly labels while maintaining ‎simplicity and low computational cost. Currently, the model is limited to four module configurations, and the use of synthetic data may lead ‎to overfitting when applied to real-world scenarios. Nevertheless, the methodology can be adapted to grid configurations with other known ‎parameters. This work provides a practical basis for integrating early anomaly detection systems into PV installations, increasing operational ‎efficiency and reducing maintenance costs.