Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 52-56.doi: 10.11896/jsjkx.191100210

• Artificial Intelligence • Previous Articles     Next Articles

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).

PDF (PC)

788

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

CLC Number: 

  • TP391
[1] WANG S P,ZHAO D M.Research review and prospect of power grid fault diagnosis[J].Automation of Electric Power Systems,2017,41(19):164-175.
[2] ZHANG H B,JIA K,SHI W J,et al.Network fault diagnosis based on information theory and expert system[J].Journal of Power Systems and Automation,2017,29(8):111-118.
[3] XIONG G J,SHI D Y,ZHU L.Fuzzy cell fault diagnosis based on radial basis function neural network[J].Automation of Electric Power Systems,2014,38(5):59-65.
[4] YUAN P,MAO J L,XIANG F H.Improved grid fault diagnosis based on genetic optimization BP neural network [J].Journal of Power Systems and Automation,2017,29(1):118-122.
[5] SHAN X,DAI Z M,ZHANG Z.Research and application of integrated intelligent warning system of intelligent power grid dispatching control system[J].Automation of Electric Power Systems,2015,39(1):65-72.
[6] BIAN L,BIAN C Y.Overview of intelligent methods for net-work fault diagnosis[J].Force System Protection and Control,2014(3):146-153.
[7] JIANG B,LI Z J.Power grid fault diagnosis based on BP neural network [J].Energy and Environmental Protection,2017,39(3):246-249.
[8] LIU G Y.Formalized modeling and decision-making planning of emergency plan [D].Wuhan:Huazhong University of Science and Technology,2011.
[9] LIU A J.Structural research on emergency plan of power indus-try [D].Beijing:University of Chinese Academy of Sciences,2012.
[10] CAO Y K,HE J W,BAO Z A.Research status and prospect of deep learning in the field of electric power [J].Journal of Shanghai Electric Power University,2017,33(4):341-345.
[11] DUAN Y J,LU Y S,ZHANG J,et al.Research status and prospect of deep learning in the field of control [J].Journal of Automation,2016(5):643-654.
[12] ZHOU Q Y.Syntactic analysis of natural language based ondeep learning [D].Harbin:Harbin Institute of Technology,2016.
[13] YAN Y.Research on text representation and classificationmethods based on deep learning [D].Beijing:University of Science and Technology Beijing,2016.
[14] NIU L Q.Research on text vector representation and modeling based on neural network [D].Nanjing:Nanjing University,2016.
[15] ZHAO X S,XIE B M,ZHANG H W.A power grid fault diagnosis method based on deep learning algorithm [J].Henan Science and Technology,2016(23):53-54.
[16] JIANG Q,SHEN L,ZHANG W,et al.Research on fault diagnosis method based on deep learning [J].Computer Simulation,2018,35(7):409-413.
[17] MIKOLOV T,SUTSKEVER I,CHEN K.Distributed Repre-sentations of Words and Phrases and their Compositionality[J].Advances in Neural Information Processing Systems,arXiv:1310.4546v1,2013.
[18] LIN Y,SHI X D,GUO F.A Chinese syntactic analysis based on probabilistic context-free grammar [J].Chinese Journal of Information Technology,2006,20(2):1-7.
[19] CHARNIAK E.Parsing With Context -free Grammar and Word Statistics[C]//Statistical Parsing with a Context-free Grammar and Word Statistics.Proc,1997.
[20] SOCHER R,BAUER J,MANNING C D,et al.Parsing withCompositional Vector Grammar[C]//Proceedings of 51st AnnualMeeting of the Association for Conputational Linguistics(Volume 1:Long papers).2013:455-465.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[3] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[4] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[5] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[6] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[7] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[8] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[9] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[10] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[11] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[12] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[13] ZHU Wen-tao, LAN Xian-chao, LUO Huan-lin, YUE Bing, WANG Yang. Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN [J]. Computer Science, 2022, 49(6A): 378-383.
[14] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[15] MAO Dian-hui, HUANG Hui-yu, ZHAO Shuang. Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance [J]. Computer Science, 2022, 49(6A): 523-530.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.