Identification of Inrush Current and Fault Current Based on Long Short-Term Memory Neural Network

被引：0
作者：
Zhang G. [1 ]
Liu K. [2 ]
Pu H. [1 ]
Yao F. [1 ]
Zhang S. [1 ]
机构：
[1] Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan
[2] State Grid Luoyang Power Supply Company, Henan, Luoyang
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2024年 / 58卷 / 05期
关键词：
fault current identification; inrush current identification; long short-term memory (LSTM) neural network; transformer differential protection;
D O I：
10.16183/j.cnki.jsjtu.2022.352
中图分类号：
学科分类号：
摘要：
The problem of differential protection maloperation caused by inrush current during no-load closing of transformer has not been completely solved so far. To solve this problem, a method using long short-term memory (LSTM) neural network to identify inrush current and fault current is proposed. First, the simulation model of no-load closing and internal fault of transformer is built on the PSCAD software platform, and a large amount of three-phase current instantaneous sampling data is generated through simulation as the sample set to train the neural network. Then, the LSTM neural network model is built and trained by using the Keras platform. Finally, the new simulation data and fault recorder data is used to test the trained LSTM neural network. The results show that the LSTM neural network can quickly and accurately distinguish the inrush current and fault current under various conditions, which proves the effectiveness of the proposed method. © 2024 Shanghai Jiaotong University. All rights reserved.
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页码：730 / 738
页数：8
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