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

Jihua WU; Pengyu ZHU; Zichen WU; Bin GU; Tao HONG; Bo GUO; Jing WANG; Jingyu WANG

doi:10.11959/j.issn.1000-0801.2021253

您当前的位置：

首页 >

文章列表页 >

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

Research and Development | 更新时间：2024-06-05

- Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining
- Telecommunications Science Vol. 37, Issue 11, Pages: 51-63(2021)
- 作者机构：
  
  1. 北京邮电大学网络与交换国家重点实验室，北京 100876
  2. 国网电力科学研究院有限公司，江苏南京 210012
  3. 国网江苏省电力有限公司信息通信分公司，江苏南京 210024
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021253
  CLC： TP393
- Published Online：2021-11，
  
  Published：20 November 2021
- 稿件说明：
移动端阅览
Jihua WU, Pengyu ZHU, Zichen WU, et al. Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining[J]. Telecommunications science, 2021, 37(11): 51-63.
DOI：

Jihua WU, Pengyu ZHU, Zichen WU, et al. Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining[J]. Telecommunications science, 2021, 37(11): 51-63. DOI： 10.11959/j.issn.1000-0801.2021253.

摘要

缺陷诊断一直是电力通信领域研究的难点之一。基于人工规则的缺陷诊断已经无法应对告警数据的海量增长。基于有监督学习的智能方法需要大量的标注数据和较长的系统构建时间，且大多面向指标性数据，实现部署缺乏可行性。面向告警数据，提出一种基于无监督聚类和频繁子图挖掘实现告警归并和缺陷模式发现的自学习算法，设计了一个自动化完成缺陷诊断及处置的架构。该架构具有良好的可扩展性和迭代更新能力，并部署于实际缺陷自动派单系统中。通过真实场景数据集进行实验验证，结果显示出良好的性能表现，实现了对缺陷的及时发现及精准派单维护。

Abstract

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.

关键词

Keywords

references

GARDNER R D , HARLE D A . Methods and systems for alarm correlation [C ] // Proceedings of Proceedings of GLOBECOM'96.1996 IEEE Global Telecommunications Conference . Piscataway:IEEE Press , 1996 : 136 - 140 .

MAZDZIARZ A . Alarm correlation in mobile telecommunications networks based on k-means cluster analysis method [J ] . Journal of Telecommunications and Information Technology , 2018 ( 2 ): 95 - 102 .

WEN L , LI X Y , GAO L , et al . A new convolutional neural network-based data-driven fault diagnosis method [J ] . IEEE Transactions on Industrial Electronics , 2018 , 65 ( 7 ): 5990 - 5998 .

XIAO F , ZHAO Y , WEN J , et al . Bayesian network based FDD strategy for variable air volume terminals [J ] . Automation in Construction , 2014 ( 41 ): 106 - 118 .

WANG J Y , JING Y H , QI Q , et al . ALSR:an adaptive label screening and relearning approach for interval-oriented anomaly detection [J ] . Expert Systems With Applications , 2019 ( 136 ): 94 - 104 .

QI Q , SHEN R Y , WANG J Y , et al . Spatial-temporal learning-based artificial intelligence for IT operations in the edge network [J ] . IEEE Network , 2021 , 35 ( 1 ): 197 - 203 .

JAKOBSON G , WEISSMAN M . Alarm correlation [J ] . IEEE Network , 1993 , 7 ( 6 ): 52 - 59 .

SCHUBERT E , SANDER J , ESTER M , et al . DBSCAN revisited,revisited [J ] . ACM Transactions on Database Systems , 2017 , 42 ( 3 ): 1 - 21 .

YANG Y C , WANG Y P , WEI Y . Adaptive density peak clustering for determinging cluster center [C ] // Proceedings of 2019 15th International Conference on Computational Intelligence and Security (CIS) . Piscataway:IEEE Press , 2019 : 182 - 186 .

BOULOUTAS A T , CALO S , FINKEL A . Alarm correlation and fault identification in communication networks [J ] . IEEE Transactions on Communications , 1994 , 42 ( 234 ): 523 - 533 .

YOUSUF H , ZAINAL A Y , ALSHURIDEH M , et al . Artificial intelligence models in power system analysis [M ] // Artificial Intelligence for Sustainable Development:Theory,Practice and Future Applications . Cham : Springer International Publishing , 2020 : 231 - 242 .

DARRAB S , ERGENC B . Vertical pattern mining algorithm for multiple support thresholds [J ] . Procedia Computer Science , 2017 ( 112 ): 417 - 426 .

HARTMANIS J . Computers and Intractability [EB ] . SIAM Review , 1982 .

YAN X F , HAN J W . gSpan:graph-based substructure pattern mining [C ] // Proceedings of 2002 IEEE International Conference on Data Mining . Piscataway:IEEE Press , 2002 : 721 - 724 .

XIONG H , LI Z M . Clustering validation measures [M ] // Data Clustering : Chapman and Hall/CRC , 2018 : 571 - 606 .

HOU J , LIU W X . Evaluating the density parameter in density peak based clustering [C ] // Proceedings of 2016 Seventh International Conference on Intelligent Control and Information Processing (ICICIP) . Piscataway:IEEE Press , 2016 : 68 - 72 .

NOWOSAD J , STEPINSKI T F . Spatial association between regionalizations using the information-theoretical V-measure [J ] . International Journal of Geographical Information Science , 2018 , 32 ( 12 ): 2386 - 2401 .

Views

338

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on power communication private network based on interleaved digital-analog hybrid modulation

Intelligent operation and maintenance of power communication network based on data perception fusion

Equipment network management security of electrical power PTN

Related Author

HU Weisheng

RUI Wenjun

XU Xiang

ZHANG Jun

Yong WU

Huahui LYU

Shengtao GUAN

Dingbo WANG

Related Institution

East China Electric Power Design Institute Co., Ltd., China Power Engineering Consulting Group

China Energy Group Taizhou Power Generation Co., Ltd.

Shanghai Jiao Tong University

Pengcheng Laboratory

Southern Power Grid Digital Grid Group Co., Ltd.

⁰