Electric field bridging pattern of pre-breakdown and breakdown condition in transformer oil

Abstract

Transformer is considered as one of the most important equipment in electrical power system networks. However, most problems occurred in transformer were related to the defects and weakness of the insulation systems. The oils used in transformer act as coolant and insulation purposes hence maintaining the dielectric strength of the transformer. In this work, electric field bridging pattern is observed from pre-breakdown and breakdown condition. The electric field bridging formation was recorded in the experimental setup and images were captured per frame. 193 images were randomly chosen from the whole video frames where 102 images were the pre-breakdown images and 91 images were the breakdown images. This system comprises of four stages: (i) a preprocessing stage to mark the electrodes tips and background subtraction; (ii) a segmentation stage to extract the electric field bridging formation in region of interest; (iii) a feature extraction stage to extract electric field bridging using feature descriptors, area, minor-axis and major-axis length   (iv) a classification stage to identify the pre-breakdown and breakdown condition. System performance was evaluated using support vector machine (SVM), k-nearest neighbour (k-NN) and random forest (RF) and SVM provided the most promising accuracy that was 99%. The results show that the combination of three feature descriptors, area, minor-axis and major-axis length are the best features combination in identifying the transformer oil condition. In future work, further studies will be conducted to investigate the pattern of pre- and post-breakdown due to some similarity found in image pattern. Due to that, more feature descriptors will be identified to find a unique pattern between pre- and post-breakdown condition