Self-tunning converter control of pmsg-based wind turbine using multi-agent deep reinforcement learning

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:	Published Online
Status:	In Press
Schools:	Schools > Engineering
Publisher:	IEEE
ISBN:	979-8-3503-7973-0
Last Modified:	10 Mar 2025 16:36
URI:	https://orca.cardiff.ac.uk/id/eprint/176779

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