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

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).