| 基于SOM神经网络的变电站设备红外热像诊断研究 |
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| 引用本文： | 王佳林,崔昊杨,许永鹏,孙运涛,张同乔,盛戈皞.基于SOM神经网络的变电站设备红外热像诊断研究[J].上海电力学院学报,2016,32(1):78-82. |
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| 作者姓名： | 王佳林 崔昊杨 许永鹏 孙运涛 张同乔 盛戈皞 |
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| 作者单位： | 上海电力学院 电子与信息工程学院, 上海 200090,上海电力学院 电子与信息工程学院, 上海 200090,上海电力学院 电子与信息工程学院, 上海 200090,国网山东省电力公司 济南供电公司, 山东 济南 250000,国网山东省电力公司 济南供电公司, 山东 济南 250000,上海交通大学 电气工程系, 上海 200240 |
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| 摘 要： | 提出了基于自组织神经网络(SOM)判别变电站设备热故障类型的红外图像诊断方法.采用了最大类间差法(OTSU)对电力设备红外热像进行了分割处理,从中提取出包括设备红外热像的温度特征值、Zernike不变矩等12个参数,以此作为设备状态识别的信息输入量,将设备的状态分类信息作为输出向量.通过训练56组红外热像数据,确定了SOM神经网络识别模型中的参数值.试验结果表明:该方法可用于变电站设备状态诊断,相对于传统的神经网络方法的诊断结果,该方法对设备运行状态评估的准确率高达85.7%,如将诊断模型产生的可疑状态列入故障状态,则故障的诊断率可达到95%以上.
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| 关 键 词： | 红外热像 SOM神经网络 故障诊断 OTSU法 |
| 收稿时间： | 2015/5/24 0:00:00 |
Infrared Image Diagnosis Method of Transformer Substation Equipment Based on SOM Neural Network |
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| WANG Jialin,CUI Haoyang,XU Yongpeng,SUN Yuntao,ZHANG Tongqiao and SHENG Gehao.Infrared Image Diagnosis Method of Transformer Substation Equipment Based on SOM Neural Network[J].Journal of Shanghai University of Electric Power,2016,32(1):78-82. |
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| Authors: | WANG Jialin CUI Haoyang XU Yongpeng SUN Yuntao ZHANG Tongqiao and SHENG Gehao |
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| Affiliation: | School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China,School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China,School of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 200090, China,Jinan Power Supply Company, Shandong Province Electric Power Company State Grid, Jinan 250000, China,Jinan Power Supply Company, Shandong Province Electric Power Company State Grid, Jinan 250000, China and Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China |
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| Abstract: | A method of diagnosing substation equipment's state based on infrared image diagnosis method of SOM neural network is proposed by using the OTSU method.The equipment's infrared thermography is obtained through thermal infrared image,which could extract temperature characteristic value,Zernike invariant moment of infrared thermography. These values can be regarded as properties of distinguishing equipment state information. Then through treating classified information of equipment as the output vector and training 56 groups of the infrared image data,SOM neural network identification model is gained,which can be utilized on diagnosis of substation equipment. The experiment results show this method is highly accurate and its accuracy rate of diagnosis of running state is 85.7%,and if suspicious state of diagnosis model is treated as fault state,the fault rate of diagnosis can be above 95%. |
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| Keywords: | infrared thermography SOM neural network Fault diagnosis OTSU method |
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