Oil-immersed Power Transformers Solid Insulation Remaining Life Study

As the key apparatus at the transformer substation, the performance of solid insulation of the oil-immersed power transformer is directly related to power system security and stability, which is attached importance to. Therefore, it is very important to monitor the remaining life of the solid insulating materials effectively.Bases on some relative references, the paper systematically explain the solid insulation aging of power transformer. From the practical work, the main work of the author in this paper is illustrated as follows:(1) Based on the relationship between the running year and CO,CO2,furfural which dissolved in the oil and the reliability of solid insulation, these data samples become to the learning and training characteristic vector matrix of the artificial neural network. Making the most of the paralleling processing, learning, memorization, nonlinearity mapping, adaptation ability and robustness etc of the artificial neural network, the author constructs the solid insulation reliability predication of the oil-immersed transformer model based on the artificial neural network.(2) In this paper, statistic analyses are carried out to reveal the relationship between the furfural contents in oil and operating years in the case of the different load, and the statistic distributing values acquired under the same operating loads are mentioned. Logistic regression is introduced to analyze the impact of furfural concentration and the dissolved gas-in-oil. The regression equation that combined the furan concentrations, the CO contents, the CO2 is given to help for judging the aging of solid insulation.(3) In this paper, the analysis of the relationship between the concentration of furfural, running length and the degree of polymerization is given a state of normal aging transformer residual life prediction model. The non-normal aging state of the residual life prediction equation is given by using the average remaining life prediction method.(4) Based on a comprehensive analysis of the above, the remaining life of transformer insulation diagnosis of solid processes and solid insulation transformer residual life prediction of the mathematical models is given. Examples show that the model of power transformer diagnosis of solid insulation aging has a good application prospects.(5) The above research results are summarized finally. The further investigative direction is put forward in the end.