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Title Fault Diagnosis of Transmission Network Based on Fusion of Time Sequence and Hierarchical Transitional WFPN

  • Shasha Zhao
  • Xiaoxiao Lin
  • Fantao Meng
  • Xuezhen Cheng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

In order to reduce the complexity of the model and improve the accuracy of the model, and make full use of the timing information of the alarm signal. A transmission network fault diagnosis method based on time sequence and hierarchical transitional WFPN was proposed. Firstly, the existing model was improved to reduce the complexity of the model, then the time correlation characteristics of component, protection and circuit breaker were constructed, and the protection and circuit breaker that did not conform to the correlation characteristics were found by time sequence reasoning. Finally, the fault diagnosis of power network was carried out by fuzzy inference. Through the analysis of typical examples, it is found that this method improves the accuracy and fault tolerance of fault diagnosis.

Keywords

Transmission network Diagnosis Time sequence Petri 

References

  1. 1.
    H.J. Cho, J.K. Park, An expert system for fault section diagnosis of power systems using fuzzy relations. IEEE Trans. Power Syst. 12(1), 342–347 (1997)CrossRefGoogle Scholar
  2. 2.
    W.A. Dos Santos Fonseca, U.H. Bezerra, M.V.A. Nunes et al., Simultaneous fault section estimation and protective device failure detection using percentage values of the protective devices alarms[J]. IEEE transactions on Power Systems, 2012, 8(1): 170-180Google Scholar
  3. 3.
    H. Xie, X. Tong, A method of synthetical fault diagnosis for power system based on fuzzy hierarchical Petri net. Power Syst. Technol. 36(1), 246–252 (2012)Google Scholar
  4. 4.
    C. Xuezhen, C. Qiang, Y. Yongjin, et al., The analytical method of power grid fault based on hierarchical transition weighted fuzzy Petri net. Trans. China Electrotech. Soc. 31(15), 125–135 (2016)Google Scholar
  5. 5.
    M. Sun, X. Tong, X. Liu et al., A power system fault diagnosis method using temporal Bayesian knowledge based. Power Syst. Technol. 38(3), 715–722 (2012)Google Scholar
  6. 6.
    J. Yang, Z. He, Power system fault diagnosis approach based on time sequence fuzzy Petri net. Autom. Electr. Power Syst. 35(15), 46–51 (2011)CrossRefGoogle Scholar
  7. 7.
    X. Tong, H. Xie, M. Sun, Power system fault diagnosis model based on layered fuzzy Petri net considering temporal constraint checking[J]. Autom. Electr. Power Syst. 37(2), 63–68 (2013)Google Scholar
  8. 8.
    X. Cheng, X. Lin, C. Zhu et al., power system fault analysis based on hierarchical fuzzy petri net considering time association character. Trans. China Electrotech. Soc. 32(14), 229–237 (2017)Google Scholar
  9. 9.
    Z. Yan, Enhanced Models and Methods for Power System Fault Diagnosis Utilizing Temporal Information of Alarm Messages (Zhejiang, Zhejiang University)Google Scholar
  10. 10.
    J. Chen, Study of Fault Diagnosis in Uncertain Power System on Information Fusion (Southwest Jiaotong University, Chengdu)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shasha Zhao
    • 1
  • Xiaoxiao Lin
    • 1
  • Fantao Meng
    • 1
  • Xuezhen Cheng
    • 1
  1. 1.College of Electrical Engineering and AutomationShandong University of Science and TechnologyQingdaoChina

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