基于GLCM和LBP的局部放电灰度图像特征提取

针对变压器局部放电模式识别中传统统计谱图特征提取维数高、识别率差等问题,提出基于灰度共生矩阵和局部二值模式的局部放电灰度图像纹理特征提取方法。该方法从宏观角度将灰度图像转化为灰度共生矩阵并获取其8维特征,从微观角度计算邻域像素相对灰度响应并获取其10维特征量。搭建四种局部放电实验模型,通过脉冲电流法采集局部放电信号;结合两类特征,以支持向量机作为分类器来识别放电类型并用传统特征提取方法作为对比。结果表明利用该方法提取灰度图像特征在避免特征灾难的同时仍有较高识别率,能有效识别四种放电模型,验证了该方法的有效性。

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 .