Overheat Fault Identification Method for Electrical Equipment Based on Three-phase Self-searching Comparison Method
-
摘要: 电力设备过热故障图谱识别是判断电力设备故障程度的重要手段。工程实际中通常是对变电设备热拍照并进行人工甄别。为提高设备热像图故障判定准确率和效率，本文针对电力三相设备的特点，提出了一种通过三相分区块自动搜寻及温度对比的过热区域判定方法，通过将三相设备热像图每相分离，调整为相似大小与姿态，将新图像分块进行对比，判定对应区块是否有异常温升，从而判定某相设备出现的热故障。试验结果表明，本文基于计算机自动搜寻和判定的设备热诊断方法能够更加高效准确地判定识别三相设备热故障，从而能够提高电力设备热故障检测的效率、准确性与自动化程度。Abstract: Overheat fault atlas identification of power equipment is an important tool for judging the fault degree of power equipment. In engineering practice, thermal photography of substation equipment and manual screening are usually carried out. To improve the heat equipment's figure fault decision accuracy and efficiency according to the characteristics of the three-phase power, this study proposes an automatic search determination method; this method involves the use of the three-phase partition piece temperature contrast of overheating area to separate every figure of the three-phase equipment heat, adjustment of images of similar size and attitude, comparing the new image block, and determining whether there is a corresponding block of abnormal temperature rise to identify the thermal faults of certain phase equipment. The test results show that the equipment thermal diagnostic method based on computer automatic search and determination can identify the thermal fault of three-phase equipment more efficiently and accurately, which improves the efficiency, accuracy, and automation degree of thermal fault detection of power equipment.
-
-
![]()
图 2 断路器三相设备连接图及其二值化
Figure 2. Circuit breaker three-phase equipment connection diagram and its binarization
![]()
图 11 每相各温度区块数量分布直方图
Figure 11. Histogram of quantity distribution of each temperature block in each phase
-
[1] 康龙. 基于红外图像处理的变电站设备故障诊断[D]. 北京: 华北电力大学, 2016. KANG Long. Substation equipment fault diagnosis based on infrared image processing[D]. Beijing: North China Electric Power University, 2016.
[2] 杨洋. 电气设备红外图像分析与处理[D]. 北京: 北京交通大学, 2015. YANG Yang. Infrared Image Analysis and Processing of Power Equipments[D]. Beijing: Beijing Jiaotong University, 2015.
[3] 王旭红, 李浩. 基于改进SSD的电力设备红外图像异常自动检测方法[J]. 电工技术学报, 2020, 35(1): 302-310. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS2020S1034.htm WANG Xuhong, LI Hao. Infrared image anomaly automatic detection method for power equipment based on improved single shot multi box detection[J]. Transactions of China Electrotechnical Society, 2020, 35(1): 302-310. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS2020S1034.htm
[4] 刘嵘, 刘辉, 贾然, 等. 一种智能型电网设备红外诊断系统的设计[J]. 红外技术, 2020, 42(12): 1198-1202. http://hwjs.nvir.cn/article/id/a00b6f68-052d-40c0-a00f-1f0ff120ce69 LIU Rong, LIU Hui, JIA Ran, et al. Design of Intelligent Infrared Diagnosis System for Power Grid Equipment[J]. Infrared Technology, 2020, 42(12): 1198-1202. http://hwjs.nvir.cn/article/id/a00b6f68-052d-40c0-a00f-1f0ff120ce69
[5] 张明. 基于红外图像的变电设备分类及在故障诊断中的应用[D]. 合肥: 合肥工业大学, 2012. ZHANG Ming. Substation Equipment Classification and Its Application in Fault Diagnosis Based on Infrared Image[D]. Hefei: Hefei University of Technology, 2012.
[6] 胡世征. 电气设备红外诊断的相对温差判断法及判断标准[J]. 电网技术, 1998, 22(10): 47-50. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS810.013.htm HU Shizheng. Method and Criterion of Relative Temperature Difference Determination of Infrared Diagnosis for Electric Eouipment[J]. Power System Technology, 1998, 22(10): 47-50. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS810.013.htm
[7] 郭英军, 孙丽华, 孙会琴, 等. 电力变压器热故障的在线诊断技术[J]. 河北科技大学学报, 2007(2): 147-149. https://www.cnki.com.cn/Article/CJFDTOTAL-HBQJ200702016.htm GUO Yingjun, SUN Lihua, SUN Huiqin, et al. An online diagnosis method of transformer hot fault[J]. Journal of Hebei University of Science and Technology, 2007(2): 147-149. https://www.cnki.com.cn/Article/CJFDTOTAL-HBQJ200702016.htm
[8] 王如意. 变电站电力设备红外图像分割技术研究[D]. 西安: 西安科技大学, 2011. WANG Ruyi. Research of Infrared Image Segmentation for Power Equipment in Substation[D]. Xi'an: Xi'an University of Science and Technology, 2011.
[9] 孙怡, 吴邦树, 刘仪卜, 等. 基于红外图像处理的电气设备故障诊断方法[J]. 机电工程技术, 2016, 45(6): 59-62. https://www.cnki.com.cn/Article/CJFDTOTAL-JXKF201606016.htm SUN Yi, WU Bangshu, LIU Yibu, et al. Method of default diagnosing of electric equipments based on infrared image processing[J]. Mechanical & Electrical Engineering Technology, 2016, 45(6): 59-62. https://www.cnki.com.cn/Article/CJFDTOTAL-JXKF201606016.htm
[10] 王淼, 杜伟, 孙鸿博, 等. 基于红外图像识别的输电线路故障诊断方法[J]. 红外技术, 2017, 39(4): 383-386. http://hwjs.nvir.cn/article/id/hwjs201704015 WANG Miao, DU Wei, SUN Hongbo, et al. Transmission Line Fault Diagnosis Method Based on Infrared Image Recognition[J]. Infrared Technology, 2017, 39(4): 383-386. http://hwjs.nvir.cn/article/id/hwjs201704015
[11] 冯振新, 许晓路, 周东国, 等. 基于局部区域聚类的电力设备故障区域提取方法[J]. 电测与仪表, 2020, 57(8): 45-50. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ202008008.htm FENG Zhenxin, XU Xiaolu, ZHOU Donggu, et al. Extraction method of power device fault region based on local clustering algorithm[J]. Electrical Measurement & Instrumentation, 2020, 57(8): 45-50. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ202008008.htm
[12] 冈萨雷斯. 数字图像处理[M]. 北京: 电子工业出版社, 2011. Rafael C. Gonzalez. Digital Image Processing[M]. Beijing: Publishing House of Electronics Industry, 2011.
-
期刊类型引用(1)
1. 左岑，杨秀杰，张捷，王璇. 基于轻量级金字塔密集残差网络的红外图像超分辨增强. 红外技术. 2021(03): 251-257 . 
本站查看
其他类型引用(0)
下载:
计量
- 文章访问数: 219
- HTML全文浏览量: 148
- PDF下载量: 29
- 被引次数: 1
