摘要
针对电力系统红外故障检测中对电力设备的人工识别效率低、实时性差等问题,提出了根据红外热图温度信息获取独立的电力设备图像,采用计算机图像处理技术实现对电力设备高效、准确识别的方法。首先,通过红外图像中的温度信息寻找设备中高温点作为种子点,采用区域生长方法有效地去除了背景,获得了整个电力设备的二值图像;然后,选取Hu不变矩作为图像特征提取方法,并对其做出了改进,计算了该二值图像的Hu不变矩,构成了电力设备的特征向量;最后,设计了BP神经网络分类器做分类识别,可用于结合温度信息实现电力系统中电力设备红外图像的故障识别。研究结果表明,该电力设备识别方法对CT、变压器、母线接头、避雷针将军帽等电力设备的识别率高、耗时少,具有良好的应用前景。
Aiming at the problems of low efficiency and poor real-time by manual of power equipment recognition in power system infrared detection, digital image processing was proposed to realize the efficient and accurate recognition based on the images obtained from the Infrared image. Firstly, the high temperature point was found as seed in power equipment from the message of infrared temperature. The background was removed effectively by region growing method to obtain the binary image of entire equipment. Secondly, Hu invariant moments and its improved algorithm were selected as the methods of feature extraction. Hu invariant moments of binary images were calculated, and feature vectors of power equipment were obtained. Finally, classifier based on BP neural network was designed to achieve different power equipment recognition, which will be used in fault diagnosis with temperature message. The research results indicate that, this method can receive a high recognition rate for different equipment and has less time-consuming, so that it will get a good prospect.
出处
《机电工程》
CAS
2013年第1期5-8,共4页
Journal of Mechanical & Electrical Engineering
基金
国家自然科学基金资助项目(5117710)
关键词
电力设备识别
红外图像
HU不变矩
区域生长
BP神经网络
power equipment identification
infrared image
Hu invariant moments
region growing
BP neural network
作者简介
陈俊佑（1988-），男，河南郑州人，主要从事基于图像处理的电网在线监测与故障诊断方面的研究．E-mail：tjchenjunyou@163．com
通信联系人：金立军，男，博士后，教授，博士生导师．E-mail：jinlj@tongji．edu．cn
