Application of knowledge graph in smart grid fault diagnosis

Author:

Liu Wentao1,Zhu Zhongxian1,Cai Kewei1,Pu Daojie1,Du Yao1

Affiliation:

1. State Grid Anhui Ultra High Voltage Company , Hefei , Anhui , China

Abstract

Abstract The stability and high quality of electricity are the basic factors which ensure that the residents and enterprises lead a happy and productive life. Therefore, in order to meet the requirements of residents’ life and enterprise production, it is necessary to improve the efficiency and accuracy of power grid fault diagnosis. In this paper, the knowledge graph is integrated into the power grid fault diagnosis, and the fault diagnosis system of the knowledge graph is constructed to realise the fault diagnosis of the power grid. The article first completes the knowledge graph construction through knowledge extraction, knowledge fusion and knowledge processing; then, it completes the construction of the fault scheduling knowledge graph through power grid equipment fault records, entity attribute extraction, coreference resolution, relation extraction, relation screening and data integration; finally, combined with the fault information knowledge analysis technology, it builds a power grid fault diagnosis system using a knowledge graph. Experiments show that using the system to diagnose the fault quantity, fault location and fault analysis information of the pilot power grid not only has ideal efficiency but also has high accuracy.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Reference30 articles.

1. Wang Jiye, DU Shuwei. (2020) Bravely stand at the forefront of the digital economy and accelerate the construction of a first-class big data center[N]. State Grid News.

2. Li Mingjie, Tao Hongzhu, Xu Hongqiang, et al. (2020) The technical framework and application prospect of artificial intelligence application in the field of power grid dispatching and control[J]. Power System Technology.

3. Shan Xin, Lu Xiao, Zhai Mingyu, et al. (2019) Analysis of key technologies for artificial intelligence applied to power grid dispatch and control[J]. Automation of Electric Power Systems.

4. Qiao Ji, Wang Xinying, Min Rui, et al. (2020) Framework and key technologies of knowledge-graph-based fault handling system in power grid[J]. Proceedings of the CSEE.

5. Rao Ziyun, Zhang Yi, Liu Juntao, et al. (2020) Recommendation methods and systems using knowledge graph[J/OL]. Acta Automatica, Sinica.

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3