DOI:10.35833/MPCE.2020.000267 |
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Data-driven Optimal Control Strategy for Virtual Synchronous Generator via Deep Reinforcement Learning Approach |
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Page view: 113
Net amount: 529 |
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Author:
Yushuai Li1,2,Wei Gao1,Weihang Yan3,Shuo Huang1,Rui Wang2,Vahan Gevorgian3,David Wenzhong Gao1
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Author Affiliation:
1.Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80208, USA;2.School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110004, China;3.National Renewable Energy Laboratory, Golden, USA
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Foundation: |
This work was supported by the U.S. National Science Foundation (No. 1711951). |
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Abstract: |
This paper aims at developing a data-driven optimal control strategy for virtual synchronous generator (VSG) in the scenario where no expert knowledge or requirement for system model is available. Firstly, the optimal and adaptive control problem for VSG is transformed into a reinforcement learning task. Specifically, the control variables, i.e., virtual inertia and damping factor, are defined as the actions. Meanwhile, the active power output, angular frequency and its derivative are considered as the observations. Moreover, the reward mechanism is designed based on three preset characteristic functions to quantify the control targets: ① maintaining the deviation of angular frequency within special limits; ② preserving well-damped oscillations for both the angular frequency and active power output; ③ obtaining slow frequency drop in the transient process. Next, to maximize the cumulative rewards, a decentralized deep policy gradient algorithm, which features model-free and faster convergence, is developed and employed to find the optimal control policy. With this effort, a data-driven adaptive VSG controller can be obtained. By using the proposed controller, the inverter-based distributed generator can adaptively adjust its control variables based on current observations to fulfill the expected targets in model-free fashion. Finally, simulation results validate the feasibility and effectiveness of the proposed approach. |
Keywords: |
Adaptive control ; virtual synchronous generator (VSG) ; reinforcement learning ; deep learning |
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Received:May 17, 2020
Online Time:2021/08/04 |
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