Classification of degradation in oil-impregnated cellulose insulation using texture analysis of optical microscopy images

Abstract

Oil-impregnated Kraft paper samples normally used for power transformer electrical insulation have been artificially aged thermally to produce a sample set with varying levels of insulation deterioration. The samples were aged in an oven at a temperature above the normal operating temperature of a power transformer.Digital images obtained from optical microscopy measurements on the paper samples have been analyzed using a texture analysis method which converts each image into a spatial gray level dependence matrix (SGLDM). The SGLDM contains information about the statistical variation of pixels gray-level intensities in an image and thereby information about the sample texture. Mathematical operators applied to the SGLDM have been used to extract 22 different statistical texture features for each sample image.Optical microscopy images of the thermally aged samples show that thermal deterioration of the insulation paper produces changes in morphology and physical structure. These changes are detectable by the statistical texture features extracted from the SGLDM texture analysis. Statistical classification is performed on the feature set to demonstrate that differentiation between oil-impregnated paper samples with different levels of thermal degradation is reliable with low error rates. Therefore, development of a practical method to assess condition of oil-impregnated paper insulation using optical microscopy and texture analysis is promising.