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Zhang, K., Yuan, F., Guo, J. and Wang, G. (2016) A Novel Neural Network Approach to Transformer Fault Diagnosis Based on Momentum-Embedded BP Neural Network Optimized by Genetic Algorithm and Fuzzy C-Means. Arabian Journal for Science and Engineering, 9, 1-11. https://doi.org/10.1016/j.msea.2016.04.010

has been cited by the following article:

• TITLE: A Transformer Replacement Decision Method Based on Probability Assessment of Failure Rate

  AUTHORS: Gang Liang, Shengwei Li, Yan Qi, Jing Cao, Yi Hao, Wenfu Chen

  KEYWORDS: Transformer Replacement, Probability Assessment, Weibull Distribution, Degree of Polymerization

  JOURNAL NAME: Energy and Power Engineering, Vol.9 No.4B, April 17, 2017

  ABSTRACT: Reasonable probability assessment of transformer failure rate (FR) is a critical reference to the transformer replacement work. At present, the lack of support theory for transformer replacement usually causes reliability and economy issues for power companies. For this reason, a transformer replacement decision method based on probability assessment of FR is proposed. Firstly, a first order model of transformer paper degradation is proposed. Then, the Weibull Distribution is used by Monte Carlo Simulation (MCS) to generate the variations of Degree of Polymerization (DP) along with time based on the historical data, and the transformer FR is determined. When the FR is higher than a pre-defined threshold value, the transformer should be replaced for reliability purpose. Finally, the effectiveness of the proposed method for the transformer replacement decision is verified by a typical engineering application.