PSO-optimized ensemble learning framework for high-accuracy sensorless voltage estimation in industrial inverter drives

Abstract Accurate voltage estimation is essential for real-time control and condition monitoring of industrial inverter-driven systems, particularly under dynamic operating conditions. This study proposes a sensorless, data-driven framework for estimating phase voltages in a three-phase inverter using advanced machine learning techniques uniquely integrating PSO-driven hyperparameter optimization within a stacking ensemble architecture, an approach not previously explored for inverter voltage estimation. The proposed approach is validated using a comprehensive experimental dataset comprising approximately 235,000 samples, collected under both steady-state and transient conditions. Key input features include DC-link voltage, input voltage, motor speed, phase currents, and Pulse-Width Modulation (PWM) duty ratios. Among individual models, XGBoost demonstrates superior performance with an RMSE of 2.97 V, MAE of 2.05 V, and R 2 of 0.999, outperforming Decision Tree, Random Forest, Gradient Boosting, and neural network models. To further enhance prediction accuracy, a stacking-based ensemble framework is developed and optimized using Particle Swarm Optimization (PSO), employing 7 particles over a maximum of 20 iterations with early stopping after 5 non-improving iterations. The final optimized ensemble achieves an improved RMSE of 2.87 V, MAE of 2.04 V, and R 2 of 0.999, with prediction errors consistently below 1.6% across seventeen test cases spanning a voltage range of 28–360 V. Compared to conventional model-based approaches that require precise motor parameter knowledge, the proposed framework effectively captures nonlinear inverter dynamics and operational variability, demonstrating strong potential for robust sensorless industrial applications. This work supports SDG 9 (Industry, Innovation and Infrastructure) by enabling intelligent, reliable, and cost-effective power electronic systems.