Estimation of Oil-Paper Insulation of Transformer Using Optical Coherence Tomography: an Image Texture Feature-Based Analysis

This article proposes an image texture featurebased algorithm for determining the ageing of oil-impregnated paper insulation of power transformers through the analysis of subsurface structural images obtained via optical coherence tomography (OCT). OCT used in this paper is a non-invasive optical diagnostic technique used for in-vivo cross-sectional imaging of paper insulation. In this research, fresh mineral oil, copper strips and kraft paper were used to prepare the oil-paper insulation samples emulating real-life insulation of a power transformer. Following this, accelerated thermal ageing of oilpaper insulation has been done by placing the samples inside a hot-air oven around 100° C for 200 hours and 400 hours, respectively. OCT based imaging of the samples has been done to capture subsurface microstructural phenomena showcasing degradation of oil-paper insulation under accelerated thermal ageing test. For texture-based feature analysis, local binary pattern and grey level co-occurrence matrix have been employed. Qualitative and quantitative analysis have been carried out to investigate the variation in aging performance and various features are determined to assess different aging states of oil-paper insulation. The outcome of the present research shows the productiveness of OCT based imaging technique, indicating its strong potential for condition assessment in oil- paper insulation in power industries.