基于远程激光成像的电力系统热故障检测技术
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摘要
采用传统的传感检测方法进行电力系统热故障检测和特征提取中存在扰动误差较大和检测实时性不好的问题,为了提高电力系统热故障检测的准确性,提出一种基于远程激光成像的电力系统热故障检测方法。采用激光传感成像技术进行电力系统的工况状态特征采集,对采集的电力系统激光图像进行分块融合和模板匹配,实现对电力系统故障区域和正常工况区域的分块处理,对分块后的图像采用边缘轮廓检测方法进行图像分割,提取电力系统热故障的像素特征,根据提取的特征量进行故障属性分类判别。仿真结果表明,采用该方法进行的电力系统热故障检测的可视化效果较好,故障检测的准确概率较高,实时性较好。
The perturbation problem of large error and bad real-time detection of power system thermal fault detection and feature extraction in the sensor with the traditional detection methods,in order to improve the accuracy of thermal power system fault detection,a method is proposed to detect faults in power system based on thermal remote laser imaging. The working state of power system data acquisition by using the laser sensing imaging technology,the power system of laser image block fusion and template matching,realize the block processing of power system fault area and normal operating area,the image block after the edge detection method for image segmentation,pixel feature extraction of power system thermal fault,according to the characteristics of volume the extraction of fault attribute classification.The simulation results show that the visualization of power system fault detection using the method of heat. The effect is better,the accuracy of fault detection is higher,and the real time is better.
The perturbation problem of large error and bad real-time detection of power system thermal fault detection and feature extraction in the sensor with the traditional detection methods,in order to improve the accuracy of thermal power system fault detection,a method is proposed to detect faults in power system based on thermal remote laser imaging. The working state of power system data acquisition by using the laser sensing imaging technology,the power system of laser image block fusion and template matching,realize the block processing of power system fault area and normal operating area,the image block after the edge detection method for image segmentation,pixel feature extraction of power system thermal fault,according to the characteristics of volume the extraction of fault attribute classification.The simulation results show that the visualization of power system fault detection using the method of heat. The effect is better,the accuracy of fault detection is higher,and the real time is better.
引文
[1]张宏利,刘吉成,李栋,等.基于免疫机理的设备异常度检测与故障诊断快速融合方法研究[J].机械设计与制造工程2016,45(11):96-101.
[2]温阳东,宋阳,王颖鑫,等.基于模糊神经网络的电力变压器故障诊断[J].计算机测量与控制,2013,21(1):39-41.
[3]孙海亮,訾艳阳,何正嘉.多小波自适应分块阈值降噪及其在轧机齿轮故障诊断中的应用[J].振动工程学报,2013,26(1):127-134.
[4] TENGFEI L,WEILI J. Automatic line segment registration using Gaussian mixture model and expectation-maximization algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(5):1688-1699.
[5] SIWEI L,XUNYU P,XING Z. Exposing region splicing forgeries with blind local noise estimation[J]. International Journal of Computer Vision,2014,110(2):202-221.
[6] GRIMM F,NAROS G,GHARABAGHI A. Closed-loop task difficulty adaptation during virtual reality reach-tograsp training assisted with an exoskeleton for stroke rehabilitation[J]. Frontiers in Neuroscience,2016,10:518.
[7]罗洁思,于德介,彭富强.齿轮箱故障振动信号的阶比多尺度形态学解调[J].机械工程学报,2010,46(11):114-120,128.
[8]李兵,徐榕,贾春宁,等.基于自适应形态提升小波与改进非负矩阵分解的发动机故障诊断方法[J].兵工学报,2013,34(3):353-360.
[9]罗琦,朱敏,韦香.多频信号的自适应随机共振检测方法[J].武汉大学学报(理学版),2013,59(3):260-266.
[10]李明爱,崔燕,杨金福,等.基于HHT和CSSD的多域融合自适应脑电特征提取方法[J].电子学报,2013,41(12):2479-2486.
[11] MUKAINO M,ONO T,SHINDO K,et al. Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke[J]. Journal of Rehabilitation Medicine,2014,46(4):378-82.
[12]王波,孔维祥,尤新刚.利用颜色滤波矩阵估计原图的拼接定位盲取证[J].电子与信息学报,2012,34(1):200-206.
[13]郭丙华,岑志松.小波去噪和神经网络相融合的超分辨率图像重建[J].激光杂志,2016,37(2):61-64.
[14]潘宗序,禹晶,胡少兴,等.基于多尺度结构自相似性的单幅图像超分辨率算法[J].自动化学报,2014,40(4):594-603.
[15]谭海鹏,曾炫杰,牛四杰,等.基于正则化约束的遥感图像多尺度去模糊[J].中国图象图形学报,2015,20(3):386-394.
[16] MEHER S K. Recursive and noise-exclusive fuzzy switching median filter for impulse noise reduction[J]. Engineering Applications of Artificial Intelligence,2014,30:145-154.
[17] HSIEH M H,CHENG F C,SHIE M C,et al. Fast and efficient median filter for removing 1-99%levels of salt-andpepper noise in images[J]. Engineering Applications of Artificial Intelligence,2013,26(4):1333-1338.
[2]温阳东,宋阳,王颖鑫,等.基于模糊神经网络的电力变压器故障诊断[J].计算机测量与控制,2013,21(1):39-41.
[3]孙海亮,訾艳阳,何正嘉.多小波自适应分块阈值降噪及其在轧机齿轮故障诊断中的应用[J].振动工程学报,2013,26(1):127-134.
[4] TENGFEI L,WEILI J. Automatic line segment registration using Gaussian mixture model and expectation-maximization algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(5):1688-1699.
[5] SIWEI L,XUNYU P,XING Z. Exposing region splicing forgeries with blind local noise estimation[J]. International Journal of Computer Vision,2014,110(2):202-221.
[6] GRIMM F,NAROS G,GHARABAGHI A. Closed-loop task difficulty adaptation during virtual reality reach-tograsp training assisted with an exoskeleton for stroke rehabilitation[J]. Frontiers in Neuroscience,2016,10:518.
[7]罗洁思,于德介,彭富强.齿轮箱故障振动信号的阶比多尺度形态学解调[J].机械工程学报,2010,46(11):114-120,128.
[8]李兵,徐榕,贾春宁,等.基于自适应形态提升小波与改进非负矩阵分解的发动机故障诊断方法[J].兵工学报,2013,34(3):353-360.
[9]罗琦,朱敏,韦香.多频信号的自适应随机共振检测方法[J].武汉大学学报(理学版),2013,59(3):260-266.
[10]李明爱,崔燕,杨金福,等.基于HHT和CSSD的多域融合自适应脑电特征提取方法[J].电子学报,2013,41(12):2479-2486.
[11] MUKAINO M,ONO T,SHINDO K,et al. Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke[J]. Journal of Rehabilitation Medicine,2014,46(4):378-82.
[12]王波,孔维祥,尤新刚.利用颜色滤波矩阵估计原图的拼接定位盲取证[J].电子与信息学报,2012,34(1):200-206.
[13]郭丙华,岑志松.小波去噪和神经网络相融合的超分辨率图像重建[J].激光杂志,2016,37(2):61-64.
[14]潘宗序,禹晶,胡少兴,等.基于多尺度结构自相似性的单幅图像超分辨率算法[J].自动化学报,2014,40(4):594-603.
[15]谭海鹏,曾炫杰,牛四杰,等.基于正则化约束的遥感图像多尺度去模糊[J].中国图象图形学报,2015,20(3):386-394.
[16] MEHER S K. Recursive and noise-exclusive fuzzy switching median filter for impulse noise reduction[J]. Engineering Applications of Artificial Intelligence,2014,30:145-154.
[17] HSIEH M H,CHENG F C,SHIE M C,et al. Fast and efficient median filter for removing 1-99%levels of salt-andpepper noise in images[J]. Engineering Applications of Artificial Intelligence,2013,26(4):1333-1338.