A Regression‐Based Approach to Sector Detection for Three‐Level NPC Inverter Using Space Vector Pulse Width Modulation

ABSTRACT Inverters are essential for reducing total harmonic distortion (THD) and power losses, while improving system efficiency. In this study, a three‐level neutral point clamped (NPC) inverter using space vector pulse width modulation (SVPWM) is presented. The primary objective is to perform regression‐based sector prediction for a three‐level NPC inverter. Sector prediction performance is evaluated using linear, exponential, and polynomial regression methods. The linear regression achieved the best performance with 0.9722 R 2 , 0.2452 RMSE, and 0.2846 MAE, outperforming the polynomial (0.9586 R 2 ) and exponential (0.8712 R 2 ) models. Quantitatively, the linear model provides a 14.4% accuracy improvement over the polynomial model and a 51.5% improvement over the exponential model. Furthermore, the inverter output shows low distortion, achieving 0.29% THD in simulation and 1.9% THD in experimental measurements. The model is analyzed for output current, voltage, and THD. The results show a high consistency between simulation, experimental validation, and regression models. This approach reduces the computational complexity of three‐level NPC inverters. The results confirm both the theoretical accuracy and practical applicability of the proposed model and illustrate its potential to improve power conversion efficiency and power quality.