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In this paper, a method for diagnosing defects inside insulated tubular busbars based on LDA optimized multi-scale texture features is proposed to help to guarantee stable operation of the tubular busbars and the whole power grid. Firstly, multi-scale PRPD spectrum space was built with the UHF discharge signals of different defects by image pyramid theory. Then first-order, second-order and higher-order texture statistics were extracted from each image in the multi-scale PRPD spectrum space to form multi-scale texture features and LDA algorithm was used to optimize the features. The method was used to make texture features contain more information about partial discharge and help to improve the accuracy of diagnosis. Experiments were conducted on a 40.5kV insulated tubular busbar and CART classification trees were established as a classifier to identify the types of defects. The results of experiments show that this method can identify the defects of insulated tubular busbars accurately.

LDA Optimized Multi-scale Texture Features Based Diagnosis Method of Defects inside Insulated Tubular Busbars