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A Deep Reinforcement Learning Optimization Method Considering Network Node Failures

Author

Listed:
  • Xueying Ding

    (State Grid Information and Telecommunication Group Co., Ltd., Beijing 100029, China)

  • Xiao Liao

    (State Grid Information and Telecommunication Group Co., Ltd., Beijing 100029, China)

  • Wei Cui

    (State Grid Information and Telecommunication Group Co., Ltd., Beijing 100029, China)

  • Xiangliang Meng

    (State Grid Information and Telecommunication Group Co., Ltd., Beijing 100029, China)

  • Ruosong Liu

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Qingshan Ye

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Donghe Li

    (School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Nowadays, the microgrid system is characterized by a diversification of power factors and a complex network structure. Existing studies on microgrid fault diagnosis and troubleshooting mostly focus on the fault detection and operation optimization of a single power device. However, for increasingly complex microgrid systems, it becomes increasingly challenging to effectively contain faults within a specific spatiotemporal range. This can lead to the spread of power faults, posing great harm to the safety of the microgrid. The topology optimization of the microgrid based on deep reinforcement learning proposed in this paper starts from the overall power grid and aims to minimize the overall failure rate of the microgrid by optimizing the topology of the power grid. This approach can limit internal faults within a small range, greatly improving the safety and reliability of microgrid operation. The method proposed in this paper can optimize the network topology for the single node fault and multi-node fault, reducing the influence range of the node fault by 21% and 58%, respectively.

Suggested Citation

  • Xueying Ding & Xiao Liao & Wei Cui & Xiangliang Meng & Ruosong Liu & Qingshan Ye & Donghe Li, 2024. "A Deep Reinforcement Learning Optimization Method Considering Network Node Failures," Energies, MDPI, vol. 17(17), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4471-:d:1472458
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    References listed on IDEAS

    as
    1. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
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