Adaptive, Optimal, Virtual Synchronous Generator Control of Three-Phase Grid-Connected Inverters Under Different Grid Conditions—An Adaptive Dynamic Programming Approach
Adaptive, Optimal, Virtual Synchronous Generator Control of Three-Phase Grid-Connected Inverters Under Different Grid Conditions—An Adaptive Dynamic Programming Approach
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DOI：
10.1109/tii.2021.3138893
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发表时间：
2022-11
影响因子：
12.3
通讯作者：
Zhongyang Wang;Yunjun Yu;Weinan Gao;M. Davari;Chao Deng
中科院分区：
文献类型：
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作者：
Zhongyang Wang;Yunjun Yu;Weinan Gao;M. Davari;Chao Deng
针对虚拟同步发电机中的三相并网逆变器，提出了一种基于强化学习和自适应动态规划的数据驱动自适应最优控制方法。本文考虑了未知的系统动态和不同的电网条件，包括平衡/不平衡电网、电压跌落/暂降和弱电网。该方法基于数值迭代，不依赖于初始的允许控制策略进行学习。在考虑VSG控制应稳定闭环系统动态的前提下，通过本文提出的自适应最优控制策略对VSG输出进行最优调节。对比仿真和实验结果验证了该方法的有效性，揭示了该方法的实用性和可实施性。
This article proposes an adaptive, optimal, data-driven control approach based on reinforcement learning and adaptive dynamic programming to the three-phase grid-connected inverter employed in virtual synchronous generators (VSGs). This article takes into account unknown system dynamics and different grid conditions, including balanced/unbalanced grids, voltage drop/sag, and weak grids. The proposed method is based on value iteration, which does not rely on an initial admissible control policy for learning. Considering the premise that the VSG control should stabilize the closed-loop dynamics, the VSG outputs are optimally regulated through the adaptive, optimal control strategy proposed in this article. Comparative simulations and experimental results validate the proposed method's effectiveness and reveal its practicality and implementation.