Adaptive Control of VSG Inertia Damping Based on MADDPG

Demu Zhang, Jing Zhang, Yu He (), Tao Shen and Xingyan Liu
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Demu Zhang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Jing Zhang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Yu He: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Tao Shen: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Xingyan Liu: Power Grid Planning and Research Center of Guizhou Power Grid Co., Ltd., Guiyang 550002, China

Energies, 2024, vol. 17, issue 24, 1-16

Abstract: As renewable energy sources become more integrated into the power grid, traditional virtual synchronous generator (VSG) control strategies have become inadequate for the current low-damping, low-inertia power systems. Therefore, this paper proposes a VSG inertia and damping adaptive control method based on multi-agent deep deterministic policy gradient (MADDPG). The paper first introduces the working principles of virtual synchronous generators and establishes a corresponding VSG model. Based on this model, the influence of variations in virtual inertia ( J ) and damping ( D ) coefficients on fluctuations in active power output is examined, defining the action space for J and D . The proposed method is mainly divided into two phases: “centralized training and decentralized execution”. In the centralized training phase, each agent’s critic network shares global observation and action information to guide the actor network in policy optimization. In the decentralized execution phase, agents observe frequency deviations and the rate at which angular frequency changes, using reinforcement learning algorithms to adjust the virtual inertia J and damping coefficient D in real time. Finally, the effectiveness of the proposed MADDPG control strategy is validated through comparison with adaptive control and DDPG control methods.

Keywords: VSG; multi-agent; deep deterministic policy gradient; frequency control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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