Self-Tunning Converter Control of PMSG-Based Wind Turbine Using Multi-Agent Deep Reinforcement Learning

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.