Binyu Cui | Searcharxiv

arXiv2025

Instantaneous Core Loss -- Cycle-by-cycle Modeling of Power Magnetics in PWM DC-AC Converters

Nowadays, PWM excitation is one of the most common waveforms seen by magnetic components in power electronic converters. Core loss modelling approaches such as improved Generalized Steinmetz equation (iGSE) or the loss map based on composite waveform hypothesis (CWH) process the PWM excitation piecewisely, which is proven to be effective for DC DC converters. As the additional challenge in PWM DC AC converters, the fundamental-frequency sinewave component induces the "major loop loss" on top of the piecewise high-frequency segments, which however cannot be modelled on a switching cycle basis by any existing methods. To address this gap, this paper proposes a novel fundamental concept, instantaneous core loss, which is the time-domain core loss observed experimentally for the first time in history. Extending the reactive voltage cancellation concept, this work presents a method to measure the instantaneous core loss, which only contains real power loss, as a function of time. Based on measurements in evaluated soft magnetic components, it was discovered that the discharging stage exhibits higher core loss than the charging stage. A modelling approach is then proposed to break down the major loop core loss, typically an average value in the literature, into the time domain to enable cycle-by-cycle modelling of core losses in PWM converters. This work enhances the fundamental understanding of the core loss process by moving from the average model to the time-domain model.

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arXiv2025

Machine Learning Based Probe Skew Correction for High-frequency BH Loop Measurements

Experimental characterization of magnetic components has grown to be increasingly important to understand and model their behaviours in high-frequency PWM converters. The BH loop measurement is the only available approach to separate the core loss as an electrical method, which, however, is susceptive to the probe phase skew. As an alternative to the regular de-skew approaches based on hardware, this work proposes a novel machine-learning-based method to identify and correct the probe skew, which builds on the newly discovered correlation between the skew and the shape/trajectory of the measured BH loop. A special technique is proposed to artificially generate skewed images from measured waveforms as augmented training sets. A machine learning pipeline is developed with the Convolutional Neural Network (CNN) to treat the problem as an image-based prediction task. The trained model has demonstrated a high accuracy and generalizability in identifying the skew value from a BH loop unseen by the model, which enables the compensation of the skew to yield the corrected core loss value and BH loop.

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