Physics-Informed Data-Driven Transmission Line Fault Location Based on Dynamic State Estimation

doi:10.1109/PESGM48719.2022.9916695

	Physics-Informed Data-Driven Transmission Line Fault Location Based on Dynamic State Estimation
	Xing, Yiqi; Liu, Yu; Wang, Binglin; Yi, Lihui; He, Xuming
	2022-07-14
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/PESGM48719.2022.9916695
摘要	Accurate fault location methods are of great significance for power restoration after system faults. This paper proposes a physics-informed data-driven transmission line fault location method. Two terminal voltage and current sampled value (SV) measurements are typically required. Traditional data-driven methods typically do not consider physical information embedded within the system. Instead, this paper builds the dynamic line model and utilizes the powerful tool of dynamic state estimation (DSE) to track system transients during faults. The fault-related features are extracted via DSE and are utilized as the inputs of the data-driven network to achieve fault location. Numerical experiments in a 500kV AC transmission line system show that the proposed physics-informed data-driven method has higher fault location accuracy in comparison to the traditional data-driven methods without consideration of physics information. The proposed method only needs the fault data window of 5ms after the occurrence of the fault, which is suitable for lines equipped with fast tripping relays. The proposed method is compatible with IEC61850-9-2 standard as it only requires SV measurements with a relatively low sampling rate of 80 samples/cycle. Moreover, although the dynamic line model is utilized for consideration of physics information, the proposed method shows strong robustness against parameter errors.
关键词	Data-driven dynamic state estimation fault location physics-informed
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/251427
专题	信息科学与技术学院_博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_硕士生信息科学与技术学院_本科生
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,Wang, Binglin,et al. Physics-Informed Data-Driven Transmission Line Fault Location Based on Dynamic State Estimation[C],2022.