Using Machine Learning to Correct Probe Skew in High-frequency Electrical Loss Measurements

JGI Seed Corn Funding Project Blog 2023/24: Jun Wang & Song Liu

Introduction

This project develops a machine learning approach to address the probe skew problem in high-frequency electrical loss measurements.

A pipeline using ML model to correct the probe skew in measuring a magnetic hysterysis loop. Model training and model deploy method shown — *Fig.1 (main figure) A pipeline using ML model to correct the probe skew in measuring a magnetic hysterysis loop*

What were the aims of the seed corn project?

To tackle the net-zero challenge through electrification, power electronic converters play an important role in modern electrical systems, such as electric vehicles and utility grids. Accurate characterisation of individual component’s loss is essential for the virtual prototyping and digital twins of these converters. Making loss measurements requires using two different probes, one voltage and one current, each with its own propagation delay. The difference in the delays between the probes, known as skew, causes inaccurate timing measurements which leads to incorrect loss measurements. Incorrectly measured loss will misinform the design process and the digital twin, which can lead to wrongly sized cooling component and potential failure of the converter systems in safety-critical applications, e.g. electric passenger cars.

As the aim of this project, we proposed to develop a Machine Learning based solution learn from experimentally measured datasets and subsequently generate a prediction model to compensate the probe skew problem. This interdisciplinary project treats the challenge as an image recognition problem with special shape constraints. The goal is a tool developed for the engineering community which takes in raw measurements and outputs the corrected data/image.

What was achieved?

Joint research efforts were made by the interdisciplinary team across two schools (EEME and Mathematics) for this project with the following achievements:

We have explored the options and made a design choice to utilize an open-source database as the foundation for this project (MagNet database produced by PowerLab Princeton), which provides rich datasets of experimentally measured waveforms. We then have developed an approach to artificially augment the data to create training data for our ML model.

We successfully developed a shape-aware ML algorithm based on the Convolutional Neural Network to capture the shape irregularity in measured waveforms and find its complex correlation to the probe skew in nanoseconds.

We subsequently develop a post-processing approach to retrospectively compensate the skew and reproduce the corrected image/data.

We evaluated the proposed ML-based method against testing datasets, which demonstrated a high accuracy and effectiveness. We also tested the model on our own testing rig in the laboratory as a real-life use case.

We have developed a web-based demonstrator to visualise the process and showcase the correction tool’s capability to the public. The web demonstrator is hosted on Streamlit and accessible through this link.

Snapshot of the web application demo showing phase shift prediction at 620 test index — *Fig.2 Snapshot of the web application demo*

Future plans for the project

This completed project is revolutionary in terms of applying ML to correct the imperfection of hardware instruments through software-based post-processing, in contrast to conventional calibration approaches using physical tools. This pilot project will initiate a long-term stream of research area leveraging ML/AI to solve challenges in power electronics engineering. The proposed method can be packaged into a software tool as direct replacement/alternative to commercial calibration tools, which cost ~£1000 each unit. Our plans for the next steps include