[1]周友维,姚建刚,王 欣,等.基于时间序列模型的劣化绝缘子红外热像检测方法[J].电瓷避雷器,2020,(01):149-155.[doi:10.16188/j.isa.1003-8337.2020.01.025]
 ZHOU Youwei,YAO Jiangang,WANG Xin,et al.Infrared Image Detection for Faulty Insulators Based on Time Series Model[J].,2020,(01):149-155.[doi:10.16188/j.isa.1003-8337.2020.01.025]
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基于时间序列模型的劣化绝缘子红外热像检测方法()
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《电瓷避雷器》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2020年01期
页码:
149-155
栏目:
绝缘子
出版日期:
2020-02-20

文章信息/Info

Title:
Infrared Image Detection for Faulty Insulators Based on Time Series Model
作者:
周友维1 姚建刚1 王 欣1 李凯迪1 刘正庭1 陆倚鹏1 尹骏刚2
(1.湖南大学电气与信息工程学院,长沙 410082; 2.湖大华龙电气与信息技术有限公司,长沙, 10285)
Author(s):
ZHOU Youwei YAO Jiangang WANG Xin LI Kaidi LIU Zhengting LU Yipeng YIN Jungang2
(1.College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; 2.Hunan HDHL Electric & Information Technology Company Limited, Changsha 410285, China)
关键词:
绝缘子 时间序列 红外图像 图像处理 ARIMA模型
Keywords:
insulators time series infrared images image processing ARIMA model
DOI:
10.16188/j.isa.1003-8337.2020.01.025
摘要:
在使用传统红外热像法对绝缘子进行检测时,绝缘子的劣化特征并不稳定,容易导致漏检、误检。针对这个问题,本文提出了时间序列模型与红外热像处理技术相结合的劣化绝缘子识别方法。本方法使用中值滤波与线性平滑滤波优化图像质量; 采用OTSU分割法结合钢帽、盘面的形态差异将绝缘子钢帽部分进行提取,实现绝缘子钢帽温度的自动提取; 考虑到绝缘子温升受到多因素影响,采用ARIMA时间序列模型对绝缘子串温度时间序列进行拟合,对其温升变化进行预测,从预测结果中找到劣化特征明显的温度曲线进行判别; 试验结果表明,ARIMA模型对绝缘子温升的时间序列有较好的拟合效果,所提出的劣化检测方法对特征不稳定的劣化绝缘子有好的检测效果。
Abstract:
When using the traditional infrared image detection method to inspect insulators, the deterioration characteristics of the degraded insulators are not stable, which is easy to lead to the missing or wrong detection. In order to solve this problem, this paper proposes an insulators identification method combining time series model with infrared image processing technology. This method uses median filter and linear smoothing filter to optimize the image quality. The OTSU segmentation method is adopted to extract the insulators steel cap in combination with the shape difference of the steel cap and the disk, and automatically extract the temperature of the insulators steel cap. Considering the insulator temperature rise is influenced by many factors, the ARIMA time series model is taken to fit and predict the temperature series of insulator strings, then identify the temperature curve which have the obvious deterioration characteristic from the prediction results. The test results show that the ARIMA model has a good fitting effect on the time series of insulator temperature rise. The proposed degradation detection method can identify the faulty insulators with unstable characteristics very well.

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备注/Memo

备注/Memo:
收稿日期:2018-05-15作者简介:周友维(1994—),男,硕士,研究方向为高压输配电技术及电力设备智能检测。
更新日期/Last Update: 2020-02-20