Data-Driven Transmission Line Fault Location with Single-Ended Measurements and Knowledge-Aware Graph Neural Network

doi:10.1109/PESGM48719.2022.9917184

	Data-Driven Transmission Line Fault Location with Single-Ended Measurements and Knowledge-Aware Graph Neural Network
	Xing, Yiqi; Liu, Yu; Nie, Yuan; Li, Rongjie; He, Xuming
	2022-07-21
会议录名称	2022 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)
ISSN	1944-9925
发表状态	已发表
DOI	10.1109/PESGM48719.2022.9917184
摘要	Transmission line fault location is one of the essential steps to ensure power supply reliability. Traditional model based methods and traveling wave based methods have limitations such as requirements of accurate line models/parameters or high sampling rates. On the other hand, most existing data-driven methods only utilize information within the raw data and fail to adopt prior physical knowledge. This paper proposes a data-driven fault location method based on knowledge-aware graph neural network (GNN) with single-ended measurements. Firstly, for different fault types, the graph structures are carefully designed to represent the inherent relationship among the measured voltage, measured current and the fault location, to incorporate prior physical knowledge. Afterwards, the GNN is adopted to achieve line fault location. The method only requires single-ended three phase voltage and current instantaneous measurements, with a relatively low sampling rate of 80 samples/cycle according to IEC61850-9-2 standard. Numerical experiments prove that the proposed GNN based fault location method has higher fault location accuracy compared to the existing multilayer perceptron (MLP) based fault location method.
关键词	fault location graph neural network (GNN) single-ended physics-informed mode transformation
会议地点	Denver, CO, USA
会议日期	17-21 July 2022
URL	查看原文
收录类别	EI
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/251428
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_何旭明组信息科学与技术学院_PI研究组_刘宇组信息科学与技术学院_博士生
通讯作者	Liu, Yu
作者单位	School of Information Science and Technology, ShanghaiTech University, Shanghai; 201210, China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Xing, Yiqi,Liu, Yu,Nie, Yuan,et al. Data-Driven Transmission Line Fault Location with Single-Ended Measurements and Knowledge-Aware Graph Neural Network[C],2022.