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

Hang Liu, Youyuan Wang, Yi Yang, Ruijin Liao, Yujie Geng and Liwei Zhou
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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

Energies, 2017, vol. 10, issue 5, 1-15

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.

Keywords: failure probability; multi-characteristic parameters; the Weibull model; differential warning value; association rule; failure modes (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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