Residual Lifetime Evaluation of Power Transformers Based on Data Fusion and Wiener Model

Accurate assessment of Remaining Useful Lifetime (RUL) of power transformers plays an essential role in improving the reliability of power grid while reducing operating costs. A dynamical RUL prediction technique is proposed in this article through analyzing the individual difference by multi-dimensional fusion of condition monitoring data. Firstly, the on-site Dissolved Gas Analysis (DGA) measurements and the calculated Degree of Polymerization (DP) are fused with the method of Analytic Hierarchy Process (AHP) and Variable Weight Principle (VWP) to construct the power transformer Life Correlation Index (LCI). Secondly, the parameters of Wiener model are updated iteratively by Bayesian rule and Expectation Maximization (EM) algorithm, to obtain the distribution and prediction of residual transformer lifetime. The residual lifetimes of four 500 kV power transformers in operation are calculated, and the predicted remaining lifetimes are 24.73 years, 30.66 years, 2.79 years, and 0.07 years, respectively. The effectiveness and reliability are demonstrated by the worst transformer case with the predicted RUL of 0.07 years, in which a short circuit fault of that transformer happened immediately after power-off maintenance. In particular, this method is able to predict the residual lifetime of transformers in a dynamic way, which provides significant practical implications for the assessment management of power transformers.