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Deep Synchronization Control of Grid-Forming Converters: A Reinforcement Learning Approach

  • Xi'an Jiaotong University
  • University of Victoria BC

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Dear Editor, This letter proposes a deep synchronization control (DSC) method to synchronize grid-forming converters with power grids. The method involves constructing a novel controller for grid-forming converters based on the stable deep dynamics model. To enhance the performance of the controller, the dynamics model is optimized within the deep reinforcement learning (DRL) framework. Simulation results verify that the proposed method can reduce frequency deviation and improve active power responses.

Original languageEnglish
Pages (from-to)273-275
Number of pages3
JournalIEEE/CAA Journal of Automatica Sinica
Volume12
Issue number1
DOIs
StatePublished - 2025

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