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A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters

Author

Listed:
  • Hang Liu

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Youyuan Wang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Yi Yang

    (Shandong Electric Power Research Institute, Shandong Electric Power Company, Jinan 250002, China)

  • Ruijin Liao

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  • Yujie Geng

    (Shandong Electric Power Research Institute, Shandong Electric Power Company, Jinan 250002, China)

  • Liwei Zhou

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China)

Abstract

Although traditional fault diagnosis methods can qualitatively identify the failure modes for power equipment, it is difficult to evaluate the failure probability quantitatively. In this paper, a failure probability calculation method for power equipment based on multi-characteristic parameters is proposed. After collecting the historical data of different fault characteristic parameters, the distribution functions and the cumulative distribution functions of each parameter, which are applied to dispersing the parameters and calculating the differential warning values, are calculated by using the two-parameter Weibull model. To calculate the membership functions of parameters for each failure mode, the Apriori algorithm is chosen to mine the association rules between parameters and failure modes. After that, the failure probability of each failure mode is obtained by integrating the membership functions of different parameters by a weighted method, and the important weight of each parameter is calculated by the differential warning values. According to the failure probability calculation result, the series model is established to estimate the failure probability of the equipment. Finally, an application example for two 220 kV transformers is presented to show the detailed process of the method. Compared with traditional fault diagnosis methods, the calculation results not only identify the failure modes correctly, but also reflect the failure probability changing trend of the equipment accurately.

Suggested Citation

  • Hang Liu & Youyuan Wang & Yi Yang & Ruijin Liao & Yujie Geng & Liwei Zhou, 2017. "A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters," Energies, MDPI, vol. 10(5), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:704-:d:98891
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    References listed on IDEAS

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    1. Junjie Lu & Jinquan Huang & Feng Lu, 2017. "Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle," Energies, MDPI, vol. 10(1), pages 1-15, January.
    2. Feng Lu & Chunyu Jiang & Jinquan Huang & Yafan Wang & Chengxin You, 2016. "A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis," Energies, MDPI, vol. 9(10), pages 1-22, October.
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    Cited by:

    1. Jie Liu & Qiu Tang & Wei Qiu & Jun Ma & Junfeng Duan, 2021. "Probability-Based Failure Evaluation for Power Measuring Equipment," Energies, MDPI, vol. 14(12), pages 1-16, June.
    2. Gaoyang Li & Xiaohua Wang & Aijun Yang & Mingzhe Rong & Kang Yang, 2017. "Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation," Energies, MDPI, vol. 10(11), pages 1-20, November.
    3. Jakub Souček & Pavel Trnka & Jaroslav Hornak, 2017. "Proposal of Physical-Statistical Model of Thermal Aging Respecting Threshold Value," Energies, MDPI, vol. 10(8), pages 1-24, August.

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