电测与仪表2017，Vol.54Issue(1)：77-82,6.
基于GLCM和LBP的局部放电灰度图像特征提取
Feature extraction for partial discharge grayscale image based on Gray Level Co-occurrence Matrix and Local Binary Pattern
摘要
Abstract
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 .关键词
变压器局部放电/特征提取/灰度共生矩阵/局部二值模式/支持向量机Key words
transformer partial discharge/feature extraction/gray level co-occurrence matrix/local binary pattern/support vector machine分类
信息技术与安全科学引用本文复制引用
赵磊,朱永利,贾亚飞,张宁,郭小红,袁亮..基于GLCM和LBP的局部放电灰度图像特征提取[J].电测与仪表,2017,54(1):77-82,6.基金项目
中央高校基本科研业务费专项资金资助项目(2014xs74) （2014xs74）
