On the Explainability of Black Box Data-Driven Controllers for Power Electronic Converters

Subham Sahoo*, Huai Wang, Frede Blaabjerg

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review

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Abstract

This paper proposes to explain the black-box feature of data-driven machine learning (ML) models used for controlling power electronic converters for the first time. As the name suggests, their “black box” feature prevents a clear understanding of the physical insights behind these ML models. It remains a fundamental aspect, if one plans to take action based on a prediction, or deploy a new ML model. Moreover, leaked and corrupted data during the training process can easily augment unexplainable actions from them. To address these issues, we first interpret the actions of the black box models by calculating a conditional entropy for each input with respect to an output. Using this metric, the averaged relationships between each input-output can be mapped and representative conclusions are firstly drawn on identifying erroneous data. Finally, these abnormal data are then removed from the training database to improve the interpretability & classification abilities of the ML model. We illustrate our findings on the performance of a regression based learning tool used for controlling a grid-connected voltage source inverter (VSI).
Original languageEnglish
Title of host publication2021 IEEE Energy Conversion Congress and Exposition (ECCE)
PublisherIEEE
Publication date16 Nov 2021
Pages1366-1372
ISBN (Print)978-1-7281-6128-0
ISBN (Electronic)978-1-7281-5135-9
DOIs
Publication statusPublished - 16 Nov 2021
Event2021 IEEE Energy Conversion Congress and Exposition (ECCE) - Vancouver, BC, Canada
Duration: 10 Oct 202114 Oct 2021

Conference

Conference2021 IEEE Energy Conversion Congress and Exposition (ECCE)
LocationVancouver, BC, Canada
Period10/10/202114/10/2021
SeriesIEEE Energy Conversion Congress and Exposition
ISSN2329-3721

Keywords

  • Artificial Intelligence
  • Power Electronics
  • Power Electronic Converters
  • Black Box Modeling
  • Black Box Control
  • Neural Networks
  • Explainable AI
  • Machine Learning

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