A Deep Reinforcement Learning Optimization Method Considering Network Node Failures

Xueying Ding, Xiao Liao, Wei Cui, Xiangliang Meng, Ruosong Liu (), Qingshan Ye and Donghe Li
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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

Energies, 2024, vol. 17, issue 17, 1-13

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.

Keywords: microgrid; topology; deep reinforcement; electric power safety (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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