Khalid, Junaid, Uddin, Muhammad Helal, Fawad, Muhammad, Smailes, Michael, Jia, Chunjiang, Simmonds, Rebecca, Wang, Sheng  ORCID: https://orcid.org/0000-0002-2258-2633 and Liang, Jun ORCID: https://orcid.org/0000-0001-7511-449X
2025.
Self-tunning converter control of pmsg-based wind turbine using multi-agent deep reinforcement learning.
Presented at: UPEC 2024,
Cardiff, Wales,
02-06 September 2024.
Proceedings 59th International Universities Power Engineering Conference (UPEC).
IEEE,
10.1109/upec61344.2024.10892442
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Abstract
The rapid development of wind energy conversion systems (WECS) brings new trends and challenges in the wind energy market. Recently, a new paradigm in control engineering known as the networked control system has emerged as a promising alternative to conventional control systems, where deep learning techniques can be effectively utilized to enhance system performance and adaptability. This paper proposes a multi-agent deep reinforcement learning (DRL) based approach to self-tune the rotor side and grid side converter controllers of PMSG-based WECS, where traditional proportional-integral-derivative controllers are replaced with twin delayed deep deterministic policy gradient-based agents. The proposed decentralised online DRL approach does not require tuning expertise to control the system effectively. The performance of the multi-agent DRL technique is investigated under different operating conditions and results are compared with the PID controller to see the effectiveness of the proposed technique.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Publisher: | IEEE |
| ISBN: | 9798350379730 |
| Date of Acceptance: | 14 June 2024 |
| Last Modified: | 09 Oct 2025 13:08 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/176779 |
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