Feature extraction for partial discharge grayscale image based on Gray Level Co-occurrence Matrix and Local Binary Pattern
In allusion to the defects of traditional statistical spectrum feature extraction of transformer partial discharge ( PD) pattern recognition such as high dimension and low recognition accuracy , a novel method to extract the feature of PD grayscale image based on gray level co-occurrence matrix ( GLCM) and local binary pattern ( LBP) is proposed in this paper .According to the proposed method , grayscale image is transformed to GLCM to obtain 8 features of GL-CM from a macro perspective and relative grayscale response of neighbor pixels is calculated based on LBP to obtain 10 features of LBP from a micro perspective .PD signals of four experimental models are collected by using pulse cur-rent method, combining with two kinds of features , support vector machine is used as the classifier to recognize four PD types, and one traditional feature extraction method is used for comparison .The results show that the proposed method can overcome the defects of high dimension and also has a high recognition accuracy , effectively identify the four types of PD models , and verify that the proposed method is effective .
国家科技期刊平台
NSTL
万方数据
维普
