Abstract
Data-driven solutions enabled by artificial intelligence (AI) have shown great potential in power electronics. Compared with conventional control methods, data-driven control methods are flexible and efficient and have fewer requirements regarding system physical knowledge. As for control-related applications, this chapter presents AI-based control approaches to power electronics applications, covering control fundamentals, data-driven principles, practical examples, and outlooks. It starts by discussing the control fundamentals from a data-driven perspective. Then, relevant AI tools, including metaheuristic methods, fuzzy logic, and machine learning methods, are presented. Specific examples are analyzed in control optimization, a fuzzy logic–based controller, a neural network-based controller, and a reinforcement learning controller. Finally, future outlooks on the state of the art in this synergistic topic are summarized.
Original language | English |
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Title of host publication | Control of Power Electronic Converters and Systems : Volume 4 |
Number of pages | 21 |
Volume | 4 |
Publisher | Elsevier Editora |
Publication date | 1 Jan 2024 |
Pages | 219-239 |
ISBN (Print) | 9780323856232 |
ISBN (Electronic) | 9780323856225 |
DOIs | |
Publication status | Published - 1 Jan 2024 |
Bibliographical note
Publisher Copyright:© 2024 Elsevier Inc. All rights reserved.
Keywords
- Artificial intelligence
- Data-driven control
- Optimization
- Physics-informed neural network
- Scientific machine learning