197 / 2023-10-20 21:00:53
Reinforcement Learning-based Control Study of Three-phase LCL-type Photovoltaic Grid-connected Inverter
grid-connected inverter; PI control;rapidity; reinforcement learning; weak grid
Final Paper
Changzhou Yu / Hefei University;Anhui Provincial Engineering Technology Research Center of Intelligent Vehicle Control and Integrated Design Technology
Feng Xu / Hefei University;School of Advanced Manufacturing Engineering;Hefei
Haizhen Xu / Hefei University
Haiyang Diao / Hefei University
Long Shen / Hefei University
Jiaqiang Fu / Hefei University
Leilei Guo / Zhengzhou University of Light Industry
<div style="text-align:justify"> In the weak grid environment with high penetration of new energy , the traditional PI control is not fast enough, which seriously affects the performance of the grid-connected inverter system. For this reason, this paper proposes a study of three-phase LCL-type PV grid-connected inverter control based on reinforcement learning. The original current loop is replaced with a reinforcement learning module. By adjusting the reward function in reinforcement learning and a lot of training, a agent can be obtained. Under the excitation of this agent, the grid-connected inverter system will have a better performance. Finally, the current control of PV grid-connected inverter based on reinforcement learning is verified to be better by comparing with traditional PI control in simulation.</div>
Important Date
  • Conference Date

    Dec 08

    2023

    to

    Dec 10

    2023

  • Nov 01 2023

    Draft paper submission deadline

  • Dec 10 2023

    Registration deadline

Sponsored By
IEEE IAS
Organized By
Southwest Jiaotong University (SWJTU)