引用本文: | 陈焕栩,解浩,张建文,严家明.基于灰度共生矩阵纹理特征的局部放电模式识别[J].电力系统保护与控制,2018,46(5):25-30.[点击复制] |
CHEN Huanxu,XIE Hao,ZHANG Jianwen,YAN Jiaming.Partial discharge pattern recognition based on texture feature of gray level co-occurrence matrix[J].Power System Protection and Control,2018,46(5):25-30[点击复制] |
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摘要: |
传统的统计参数特征,因放电次数及放电相位分布等因素的影响,会出现无效信息,降低局部放电识别率。为了减少这些因素的影响,引入灰度共生矩阵纹理特征对局部放电进行模式识别。首先通过实验构造局部放电相位-放电量-放电次数(j-q-n)三维图谱,获得放电分布矩阵。其次计算其对应的灰度共生矩阵,并提取出相应纹理特征。最后利用聚类分析对不同放电缺陷进行了有效判别分类。实验结果表明:基于放电分布灰度共生矩阵的纹理特征可以有效辨识不同局部放电类型,为局部放电模式识别提供了一种新思路。 |
关键词: 局部放电 j-q-n三维图谱 放电分布矩阵 灰度共生矩阵 纹理特征 |
DOI:10.7667/PSPC170274 |
投稿时间:2017-02-28修订日期:2017-05-07 |
基金项目:中央高校基本科研业务费专项资金(2012QNA10);中国博士后科学基金(2013M541755) |
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Partial discharge pattern recognition based on texture feature of gray level co-occurrence matrix |
CHEN Huanxu,XIE Hao,ZHANG Jianwen,YAN Jiaming |
(School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221008, China) |
Abstract: |
For the characteristics of the traditional statistical parameters, due to the number of discharge and discharge phase distribution and other factors, there will be invalid information, and reduce the partial discharge recognition rate. In order to reduce the influence of these factors, this paper introduces the texture feature of gray-level co-occurrence matrix to make pattern recognition of partial discharge. First, the (j-q-n) three-dimensional spectrum of partial discharge phase-discharge capacity-discharge numbers is constructed through the experiment, and the discharge distribution matrix is acquired, and then the corresponding gray level co-occurrence matrix is calculated, and the corresponding texture features are extracted, finally, cluster analysis is used to classify different discharge defects effectively. Experimental results show that texture feature based on discharge distribution of gray-level co-occurrence matrix can effectively identify the different types of partial discharge, which provides a new idea for partial discharge pattern recognition. This work is supported by Fundamental Research Funds for the Central Universities (No. 2012QNA10) and China Postdoctoral Science Foundation (No. 2013M541755). |
Key words: partial discharge j-q-n three-dimensional map discharge distribution matrix gray level co-occurrence matrix texture features |