基于深度q网络的虚拟同步发电机鲁棒性自适应参数

IF 0.2 Q4 AREA STUDIES

Asia-Pacific Journal-Japan Focus Pub Date : 2022-10-28 DOI:10.1109/ITECAsia-Pacific56316.2022.9941893

Wenjie Wu, Feng Guo, Qiulong Ni, Xing Liu, Lin Qiu, Youtong Fang

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引用次数: 1

摘要

研究了一种基于强化学习的虚拟同步发电机鲁棒性参数自适应整定方法。其中，采用深度q -网络(deep Q-network, DQN)算法实现了VSG控制器中惯量和阻尼系数的实时参数整定。仿真结果验证了所提出的参数整定方法的有效性，并与常规的固定参数VSG控制器进行了比较。

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Deep Q-Network based Adaptive Robustness Parameters for Virtual Synchronous Generator

This paper investigates a reinforcement learning based adaptive robustness parameter tunning approach for the virtual synchronous generator (VSG). Particularly, a deep Q-network (DQN) algorithm is employed to realize the real-time parameter tuning of inertia and damping coefficient in the VSG controller. The proposed parameter tuning approach is confirmed by the simulation results and compared with the conventional VSG controller with fixed parameters.

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来源期刊

Asia-Pacific Journal-Japan Focus AREA STUDIES-

CiteScore

1.20

自引率

0.00%

发文量