Fault section location and fault propagation path reasoning of power grid based on LSTM

被引：0

作者：

Li Z. ^{[1
]}

Yao W. ^{[1
]}

Zeng L. ^{[1
]}

Ma S. ^{[2
]}

Wen J. ^{[1
]}

机构：

[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan

[2] China Electric Power Research Institute, Beijing

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2021年 / 41卷 / 06期

关键词：

Deep neural network; Electric power systems; Energy supply on port; Long short-term memory network; Online fault diagnosis; Sliding window;

D O I：

10.16081/j.epae.202102016

中图分类号：

学科分类号：

摘要：

To maintain the security and stability of power system after failure, it is necessary to locate the fault section quickly and determine the propagation path of the fault impact, a fault section location and fault propagation path reasoning method based on LSTM(Long Short-Term Memory network) is proposed. Firstly, two fault diagnosis models are built by LSTM to realize online fault time judgment and fault section location respectively. Then, the propagation path of the impact caused by the fault can be determined by the ESP(Energy Supply on Port) of the transmission lines near the fault. Finally, the 8-machine 36-bus power grid is taken as an example for verification, and the results show that the proposed model can detect the fault immediately after it occurs, give fault section and fault impact propagation path, and has strong robustness to noise. © 2021, Electric Power Automation Equipment Press. All right reserved.

引用

页码：164 / 170and178

共 20 条

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