基于Res-BiLSTM的柔性直流配电网故障选极
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四川轻化工大学自动化与信息工程学院

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TM721

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四川省科技厅项目(2022YFS0518,2022ZHCG0035);人工智能四川省重点实验室项目(2023RYY06);企业信息化与物联网测控技术四川省高校重点实验室项目(2022WYY04)


Fault Pole Selection for Flexible DC Distribution Grids Based on Res-BiLSTM
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1.School of Automation and Information Engineering, Sichuan University of Science &2.Engineering

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    摘要:

    针对传统的选极方法存在着抗噪、抗高阻能力弱以及阈值整定复杂等问题,提出一种基于Res-BiLSTM网络的柔性直流配电线路故障选极方法。首先,对原始故障信号进行完全自适应噪声模态分解,再采用相关系数和香农熵进行重构得到重构信号;其次,搭建Res-BiLSTM网络模型进行选极,为提高网络精度与收敛速度,在ResNeSt网络中引入通道注意力模块,并使用卷积BiLSTM网络与改进ResNeSt网络同时提取重构信号特征,使用注意力特征融合模块融合提取到的特征,并对融合特征进行分类;最后,利用PSCAD/EMTDC搭建模型并验证所提方法。仿真结果表明所提选极方法准确性高,抗干扰能力强,不受故障距离影响。

    Abstract:

    Aiming at the traditional pole selection method, which has the problems of weak noise and high resistance resistance as well as complex threshold rectification, a flexible DC distribution line fault pole selection method based on Res-BiLSTM network is proposed. Firstly, the original fault signal is subjected to complete adaptive noise modal decomposition (CEEMDAN) , and then the reconstructed signal is obtained by using the correlation coefficient and Shannon entropy for reconstruction; secondly, the Res-BiLSTM network model is constructed for the pole selection, and in order to improve the network accuracy and the convergence speed, the channel attention module is introduced into the ResNeSt network, and the convolution BiLSTM network is used with the improved ResNeStM network for the pole selection. network and the improved ResNeSt network to extract the reconstructed signal features simultaneously, and use the Attention Feature Fusion Module to fuse the extracted features and classify the fused features; finally, the model is constructed and the proposed method is verified using PSCAD/EMTDC. The simulation results show that the proposed pole selection method is highly accurate, anti-interference, and independent of fault distance.

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郑超文,吴浩,吴川兰,等. 基于Res-BiLSTM的柔性直流配电网故障选极[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-12-11
  • 最后修改日期:2024-06-19
  • 录用日期:2024-07-09
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