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Inverse application of artificial intelligence for the control of power converters

Gao, Yuan; Wang, Songda; Hussaini, Habibu; Yang, Tao; Dragicevic, Tomislav; Bozhko, Serhiy; Wheeler, Pat; Vazquez, Sergio

Authors

Yuan Gao

Songda Wang

Habibu Hussaini

TAO YANG TAO.YANG@NOTTINGHAM.AC.UK
Associate Professor

Tomislav Dragicevic

SERHIY BOZHKO serhiy.bozhko@nottingham.ac.uk
Professor of Aircraft Electric Power Systems

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PATRICK WHEELER pat.wheeler@nottingham.ac.uk
Professor of Power Electronic Systems

Sergio Vazquez



Abstract

This paper proposes a novel application method, Inverse Application of Artificial Intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of Artificial Neural Network (ANN) is discussed, an optimization-based design. Then, a two-source-converter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multi-level converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility and practicality of IAAI.

Citation

Gao, Y., Wang, S., Hussaini, H., Yang, T., Dragicevic, T., Bozhko, S., …Vazquez, S. (2022). Inverse application of artificial intelligence for the control of power converters. IEEE Transactions on Power Electronics, https://doi.org/10.1109/TPEL.2022.3209093

Journal Article Type Article
Acceptance Date Sep 12, 2022
Online Publication Date Sep 23, 2022
Publication Date Sep 23, 2022
Deposit Date Oct 11, 2022
Publicly Available Date Oct 11, 2022
Journal IEEE Transactions on Power Electronics
Print ISSN 0885-8993
Electronic ISSN 1941-0107
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/TPEL.2022.3209093
Keywords Artificial intelligence (AI); Machine learning; Droop control; Power converters; Inverse application; Artificial neural network (ANN); Droop control; Current sharing
Public URL https://nottingham-repository.worktribe.com/output/12321775
Publisher URL https://ieeexplore.ieee.org/document/9900426

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