Predictive modeling and anomaly detection in solar PV inverters using machine learning

• Developed a machine learning framework for modeling and fault detection in photovoltaic inverters. • Achieved active power prediction with R² = 0.995 and MAE = 0.12 kW using a Random Forest model. • Reached 100 % accuracy in classifying inverter output states under static operating conditions. • Applied Z-score analysis to detect anomalies during peak production hours. • One-hour-ahead forecasting reduced classification accuracy to 36.4 %. The operational stability of photovoltaic (PV) systems is critical to the success of distributed renewable energy integration. This study presents a machine learning-driven framework for performance modeling, anomaly detection, and classification of inverter output in a grid-connected PV installation. Using high-resolution data collected from 30 kW and 40 kW inverters over one month, we applied supervised learning techniques to predict active power output, categorize production levels, and detect deviations from expected behavior. A Random Forest Regressor achieved high fidelity in power prediction (R² = 0.995, MAE = 0.12 kW), while classification models categorized output levels with 100 % accuracy under static conditions. Anomaly detection using Z-score analysis identified significant outliers, particularly during high-production intervals. However, one-hour-ahead classification revealed substantial drops in predictive performance (accuracy = 36.4 %), highlighting the inherent difficulty of forecasting under variable environmental conditions. Feature importance analysis underscored the dominant role of inverter input power and phase currents in prediction accuracy. These findings demonstrate the utility of interpretable, data-driven models for real-time diagnostics and forecasting in PV systems and lay the groundwork for scalable smart monitoring infrastructures. Unlike prior studies that rely on meteorological inputs, this work uses only inverter and grid-side electrical measurements, demonstrating that interpretable models can provide actionable insights even in data-limited scenarios. The proposed framework offers a practical foundation for early-warning diagnostics and intelligent maintenance scheduling, enabling more resilient PV operations. Its integration of interpretable machine learning with real-time monitoring distinguishes it from conventional rule-based supervision methods.