Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Lixiao Mu, Xiaobing Xu, Zhanran Xia, Bin Yang, Haoran Guo, Wenjun Zhou and Chengke Zhou
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Lixiao Mu: State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, China
Xiaobing Xu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Zhanran Xia: State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, China
Bin Yang: State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, China
Haoran Guo: State Grid Hubei Electric Power Company, Wuhan Power Supply Company, Wuhan 430072, China
Wenjun Zhou: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Chengke Zhou: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2021, vol. 14, issue 14, 1-15

Abstract: Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.

Keywords: cable accessories; infrared image processing; Faster RCNN; Mean-Shift algorithm; smart condition diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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