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Artificial_Intelligence_Techniques_for_Enhancing_the_Performance_of_Controllers_in_Power_Converter-Based_SystemsAn_Overview.pdf (1.18 MB)

Artificial Intelligence Techniques for Enhancing the Performance of Controllers in Power Converter-Based Systems—An Overview

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journal contribution
posted on 2024-03-04, 12:59 authored by Y Gao, S Wang, T Dragicevic, P Wheeler, P Zanchetta

The integration of artificial intelligence (AI) techniques in power converter-based systems has the potential to revolutionize the way these systems are optimized and controlled. With the rapid advancements in AI and machine learning technologies, this article presents the analysis and evaluation of these powerful tools as well as in computational capabilities of microprocessors that control the converter. This article provides an overview of AI-based controllers, with a focus on online/offline supervised, unsupervised, and reinforcement-trained controllers. These controllers can be used to create surrogates for inner control loops, complete power converter controllers, and external supervisory or energy management control. The benefits of using AI-based controllers are discussed. AI-based controllers reduce the need for complex mathematical modeling and enable near-optimal real-time operation via computational efficiency. This can lead to increased efficiency, reliability, and scalability of power converter-based systems. By using physics-informed methods, a deeper understanding of the underlying physical processes in power converters can be achieved and the control performance can be made more robust. Finally, by using data-driven methods, the vast amounts of data generated by power converter-based systems can be leveraged to analyze the behavior of the surrounding system and thereby forming the basis for adaptive control. This article discusses several other potential disruptive impacts that AI could have on a wide variety of power converter-based systems.

Funding

Marie Skłodowska-Curie Actions

European Union's Horizon 2020 Research and Innovation Staff Exchange (Grant Number: 872001)

History

Author affiliation

College of Science & Engineering/Engineering

Version

  • VoR (Version of Record)

Published in

IEEE Open Journal of Industry Applications

Volume

4

Pagination

366 - 375

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

eissn

2644-1241

Copyright date

2023

Available date

2024-03-04

Language

en

Deposited by

Dr Yuan Gao

Deposit date

2024-02-12

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