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Ageing assessment of paper insulation is important for the safe operation, maintenance and replacement of power transformers. The life expectancy of a transformer is closely related to the ageing state of its paper insulation. This paper presents novel markers for ageing characterization of paper insulation in oil-filled transformers based on the dispersion staining colors of cellulose fibers. Through the accelerated thermal ageing experiments of oilpaper insulation at 90, 110 and 130 °C, aged oil-paper samples of different ageing degrees are obtained. The changes of physical and chemical characteristics were proven. Meanwhile, the mechanism of changes of fiber refractive index during thermal ageing was studied by molecular simulation software. The simulation results show that the refractive index of cellulose fibers detached from insulation paper increases with the increment of ageing degree of insulation paper; The increase in the refractive index of cellulose during thermal ageing was a result of the combined effect of increased bulk density and increased polarization. Subsequently, the dispersion staining colors of cellulose fibers were observed by dispersion staining method. Then, the original image of dispersion staining was preprocessed. Finally, three ageing markers were proposed which were the primary color ratio-based ratio of blue, the primary color ratio-based ratio of red and color inhomogeneity. Additionally, the aged fiber samples were identified by the method of the support vector machine (SVM) according to the three ageing markers.

Surface discharge may cause irreversible damage to turn-to-ground insulation in valve windings of converter transformer, where withstand with AC-DC combined voltage. This paper analyzes the phenomenon and characteristics of surface discharge on oil-impregnated pressboard (OIP) under AC-DC combined voltage, and develops a discharge state recognition method. The cylinder-plate discharge model was used to simulate surface discharge. The results showed that discharge development and OIP failure were significantly accelerated by white marks on OIP which were essentially gaseous channels. The discharge characteristics before and after white mark occurrence were both dominated by AC component because of its bigger contribution to electrical field distribution (EFD), and the DC component had obvious effect on accelerating OIP failure. A set of features representing discharge state after white mark occurrence was selected out by the entropy weight method (EWM), based on which the discharge process was classified into three states (stable, fast development and pre-breakdown state) by fuzzy C means clustering method (FCM). A support vector machine (SVM) classifier to recognize discharge state was trained and showed a good performance, whose average assessment accuracy was up to 91.98%. Moreover, the ratio between negative and positive discharge numbers could be used as an auxiliary indicator of pre-breakdown state.