A Deep Reinforcement Learning Design for Virtual Synchronous Generators Accommodating Modular Multilevel Converters

The deep reinforcement learning (DRL) technique has gained attention for its potential in designing “virtual network” controllers. This skill utilizes a novel solution that can avoid the specific parameters and system model required in classical dynamic programming algorithms. However, addressing th...

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Bibliographic Details
Published in:Applied sciences 2023-05, Vol.13 (10), p.5879
Main Authors: Yang, Mu, Wu, Xiaojie, Loveth, Maxwell Chiemeka
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Decision making
Deep learning
deep reinforcement learning (DRL)
Electric current converters
Mathematical models
modular multilevel converter (MMC)
Neural networks
Optimal control
Optimization
Reinforcement
Synchronous machines
Systems stability
twin delayed deep deterministic policy gradient (TD3)
Virtual networks
virtual synchronous machine (VSG)
Working conditions
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Summary:The deep reinforcement learning (DRL) technique has gained attention for its potential in designing “virtual network” controllers. This skill utilizes a novel solution that can avoid the specific parameters and system model required in classical dynamic programming algorithms. However, addressing the issue of system uncertainties and performance deterioration remains a challenge. To overcome this challenge, the authors propose a new control prototype using a twin delayed deep deterministic policy gradient (TD3)-based adaptive controller, which replaces the conventional virtual synchronous generator (VSG) module in the modular multilevel converter (MMC) control. In this approach, an adaptive programming module is developed using a critic fuzzy network point of view to determine the optimal control policy. The modification presented in this framework is able to improve the system stability and resist disruptions while retaining the merits of the conventional VSG control model. The proposed approach is implemented and tested using the DRL toolbox in MATLAB/Simulink.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13105879