Abstract

Conducted electromagnetic interference (EMI) in multilevel power converters is governed by nonlinear interactions among passive filter components, operating conditions, and resonance-sensitive spectral behavior, making analytical prediction and trial-and-error tuning insufficient for systematic compliance-oriented design. This study presents an experimentally grounded, data-driven framework for predicting and optimizing conducted EMI in an IGBT-based, SVPWM-controlled three-level neutral-point-clamped (NPC) inverter equipped with an active harmonic filter. A dataset of 1000 conducted-emission measurements was constructed from 250 filter parameter combinations evaluated under four operating scenarios: constant-current average, constant-current peak, standby average, and standby peak, over the 10 kHz–30 MHz range. Four surrogate architectures were trained and evaluated: a multilayer perceptron (ANN), a convolutional neural network (CNN), a deep neural network (DNN), and a physics-informed neural network (PINN). Model reliability was assessed through nested cross-validation, standard 5-fold cross-validation, Monte Carlo resampling, and SHAP-based interpretability analysis. Among the tested architectures, the CNN achieved the most consistent predictive performance and stability, whereas the PINN provided smoother and more physically disciplined spectral reconstructions in several load-related conditions. The trained surrogates were embedded in a Python 3.11-based graphical user interface and further employed within a compliance-oriented optimization framework to identify filter parameter sets capable of satisfying legal conducted-emission limits. Experimental verification confirmed that surrogate-guided optimized designs achieved positive worst-case legal margins between 7.26 and 11.50 dBµV. Relative to the best measured pre-optimization combination, which still exhibited a worst-case margin of −3.7 dBµV, the best experimentally validated optimized design improved the worst-case legal margin by 15.20 dBµV. These results demonstrate that experimentally trained surrogate models can support not only high-resolution EMI prediction but also regulation-aware filter design and practical engineering decision making.

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