Telecommunications Science ›› 2021, Vol. 37 ›› Issue (11): 51-63.doi: 10.11959/j.issn.1000-0801.2021253

• Research and Development • Previous Articles     Next Articles

Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining

Jihua WU1, Pengyu ZHU2, Zichen WU3, Bin GU3, Tao HONG3, Bo GUO3, Jing WANG1, Jingyu WANG1   

  1. 1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2 State Grid Electric Power Research Institute Co., Ltd, Nanjing 210012, China
    3 Information and Communication Branch of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China
  • Revised:2021-11-15 Online:2021-11-20 Published:2021-11-01
  • Supported by:
    Science and Technology Project of State Grid Corporation(5700-202040367A-0-0-00)


Fault diagnosis is one of the most challenging tasks in power communication.The fault diagnosis based on rules can no longer meet the demand of massive alarms processing.The existing approaches based on the supervised learning need large sets of the labeled data and sufficient time to train models for processing continuous data instead of alarms, which are far behind the feasibility of deployment.As for alarm correlation and fault pattern discovery, a self-learning algorithm based on the density-based clustering and frequent subgraph mining was proposed.A novel approach for automatic fault diagnosis and dispatch were also introduced, which provided the scalable and self-renewing ability and had been deployed to the automatic fault dispatch system.Experiments in the real-world datasets authorized the effectiveness for timely fault discovery and targeted fault dispatch.

Key words: power communication, fault diagnosis, unsupervised clustering, frequent subgraph mining

CLC Number: 

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