计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 52-56.doi: 10.11896/jsjkx.191100210

• 人工智能 • 上一篇    下一篇

基于深度学习的电网故障预案信息抽取研究

石赫1, 杨群1, 刘绍翰1, 李伟2,3,4   

  1. 1 南京航空航天大学计算机科学与技术学院 南京 211106
    2 南瑞集团(国网电力科学研究院)有限公司 南京 211106
    3 国电南瑞科技股份有限公司 南京 211106
    4 智能电网保护和运行控制国家重点实验室 南京 211106
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 杨群(qun.yang@nuaa.edu.cn)
  • 作者简介:657332737@qq.com
  • 基金资助:
    智能电网保护和运行控制国家重点实验室课题;江苏省重点研发计划(BE2019012)

Study on Information Extraction of Power Grid Fault Emergency Pre-plans Based on Deep Learning

SHI He1, YANG Qun1, LIU Shao-han1, LI Wei2,3,4   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2 NARI Group Corporation (State Grid Electric Power Research Institute),Nanjing 211106,China
    3 NARI Technology Co.Ltd.,Nanjing 211106,China
    4 State Key Laboratory of Smart Grid Protection and Control,Nanjing 211106,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:SHI He,born in 1995,postgraduate,is a member of China Computer Federation.Her main research interests include text mining.
    YANG Qun,born in 1971,Ph.D,professor,is a member of China Computer Federation.Her main research interests include algorithm design.
  • Supported by:
    This work was supported by the Project of “the State Key Laboratory of Smart Grid Protection and Operation Control” and Primary Research & Developement Plan of Jiang Province(BE2019012).

摘要: 为对电网调度部门保存的应急预案进行处理,以方便调度人员面临突发事件时能快速检索和匹配预案中的类似事故并借鉴以往经验处理突发事故,需要进行预案的信息抽取,提取其关键信息。传统的电网故障应急预案处理方法的通用性和可扩展性不强,无法有效实现电网故障应急预案的数字化。采用深度学习方法,对调度紧急预案的内容进行句法分析,得到分析树,在此基础上抽取出故障时系统的状态信息和对故障的处置要点,进而将非结构化的预案信息转化成结构化数据存储和管理。采用上述方法可以有效管理电网预案,辅助调度人员完成故障的判定并按预案给出的操作方法进行电网运行管理,从而提高故障处理效率。同时,该方法具有通用性好、扩展性强的优势,可以实现模型的持续改进。

关键词: 分析树, 句法分析, 深度学习, 信息抽取, 应急预案

Abstract: The emergency pre-plans are saved by the power grid dispatching department.The power grid dispatching department has formulated it based on the power grid operation and maintenance experiences,which can assist dispatchers to deal with the emergency fault.When an emergency fault occurs in the power grid,in order to effectively deal with the emergency pre-plans,so that dispatchers can learn from previous experience to handle emergencies,it's necessary to extract the key information of the pre-plan.So in this way they can quickly retrieve and match similar accidents in the per-plans.However,there are many problems of the traditional power grid fault emergency pre-plans processing method.The traditional processing method is not versatile and scalable,and it is unable to effectively digitize the power grid fault emergency pre-plans.This leads to a limited scope of application of the traditional method.In this paper,the deep learning method is used to make up for the shortcomings of traditional processing methods.It is used to analyze the syntax of the sentences for the dispatch emergency pre-plans.Then it will generate a syntactic parser and the parser will give the syntactic parse tree of the sentences.After that,the system state information and disposal points for the emergency pre-plans are extracted,and then the unstructured text information of the emergency pre-plans is transformed into the structured data.Using the deep learning method,the power grid emergency pre-plans can be effectively ma-naged,and the dispatcher can determine the fault type and carry out the operation quickly.This paper also carries out experimental verification for the method.It can be concluded that the method can improve the efficiency of fault processing,while it also has the advantages of good versatility and strong expansibility,and can achieve continuous improvement of the model.

Key words: Deep learning, Emergency pre-plans, Information extraction, Syntactic analysis, Syntactic parse tree

中图分类号: 

  • TP391
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