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| Residual Life Prediction Method of Transformer Oil-Paper Insulation Based on Binary Nonlinear Wiener Random Process |
| Zhao Hongshan, Chang Jieying, Qu Yuehan, Sun Chengyan, Guo Xiaomei |
| School of Electrical and Electronic Engineering North China Electric Power University Baoding 071000 China |
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Abstract Oil-immersed transformer is one of the core equipment of power system, and its safe and stable operation is of great significance to the reliable power supply of power grid. The purpose of performance reliability analysis and remaining life prediction of transformer is to predict its degradation trend and life end, so that maintenance measures can be taken in time before the end of transformer life to avoid power failure. The operating life of transformer mainly depends on its insulation performance, so the reliability analysis of insulation performance and the remaining life prediction are of great significance.
Most of the traditional transformer oil paper insulation degradation models were based on single performance degradation. In view of the problem that the traditional prediction method of residual life of oil-paper insulation of oil-immersed power transformer with single performance degradation is difficult to fully reflect the degradation behavior of oil-paper insulation and the lack of consideration of the correlation between multi-performance degradation, considering the randomness and nonlinearity of the degradation process fully in this paper, a method for residual life prediction of transformer oil-paper insulation based on binary nonlinear Wiener random process was proposed. Firstly, based on nonlinear Wiener stochastic process, the oil-paper insulation degradation model with single property degradation quantity was established to describe the randomness and nonlinearity of oil-paper insulation degradation process. Then, based on Copula function, a degradation model with two performance correlation was established to analyze the joint degradation behavior of transformer oil-paper insulation reflected by two performance degradation quantities more comprehensively. However, different Copula functions would produce different results. Based on AIC criterion, this paper proved that Frank Copula function had better goodness of fit when describing the correlation between furfural and methanol degradation of transformer oil-paper insulation. Finally, because the likelihood function was too complex and there were many unknown parameters, this paper used MCMC-Gibbs sampling algorithm to estimate the unknown parameters of the model and predict the remaining life of transformer oil-paper insulation.
In order to verify the effectiveness and rationality of the proposed method, the contents of furfural and methanol were taken as performance degradation quantities, and the experimental data of accelerated thermal aging were used for example verification. The reliability curves and the prediction results of remaining life were compared under three conditions of single performance, two independent performance and two correlated performance. The results show that the reliability of the single performance case is higher than that of the two-performance case, and the reliability of the performance independent case is higher than that of the performance-dependent case. The reliability assessment method of oil paper insulation for transformer based on binary method considering performance correlation is more conservative than that based on single performance reliability assessment method. By comparing the experimental data, it can be seen that the proposed method is more consistent with the actual measurement results and the evaluation results are more reasonable. By comparing the prediction results of transformer oil paper insulation remaining life at four different monitoring times based on furfural, methanol and two performance correlation cases, it can be seen that the proposed method takes into account the reliability and accuracy of the prediction results, and can provide a reference for the health status assessment of oil-immersed power transformers.
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Received: 17 May 2022
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2023, Vol. 38 
