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

TL;DR

This paper introduces a machine learning approach using CNNs to accurately identify and correct probe skew in high-frequency BH loop measurements, improving core loss estimation in magnetic component testing.

Contribution

It presents a novel CNN-based method for probe skew correction in BH loop measurements, utilizing artificially generated training data for enhanced accuracy.

Findings

High accuracy in skew detection from unseen BH loops

Effective correction of probe skew improves core loss measurement

Generalizable approach applicable to various magnetic components

Abstract

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.