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Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation

2022-09-07

上海交通大学江秀臣等：基于深度自注意力网络和多因素相似度计算的变电站设备红外图像自动识别方法

原创英文期刊编辑部全球能源互联网期刊 2022-09-07 08:00

摘要

红外图像识别是电力设备巡检任务中的关键步骤。现有技术的局限性包括手动选择的不可转移和可解释的特征，以及有限的训练数据。针对这些问题，本文提出了一种自动红外图像识别框架，包括基于深度自注意力网络的物体识别模块和基于多因素相似度计算的温度分布识别模块。首先，使用多头注意力编码-解码机制从输入图像中提取和嵌入特征。然后，嵌入特征用于预测设备组件类别和位置。对于定位区域，进行初步分割。最后将相似区域逐步合并，得到设备的温度分布，判断是否存在故障。与该任务中的相关工作相比，实验表明所提方法的准确性显着提高，为电力设备检测自动化技术提供了很好的参考。

Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation

基于深度自注意力网络和多因素相似度计算的变电站设备红外图像自动识别方法

Yaocheng Li¹, Yongpeng Xu¹, Mingkai Xu², Siyuan Wang², Zhicheng Xie³, Zhe Li¹, Xiuchen Jiang¹

(1.School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, P. R. China

2.State Grid Shandong Electric Power Company Jinan Power Supply Company, 250001 Shandong, P. R. China

3.Maintenance & Test Center of EHV power Transmission Company, China Southern Power Grid, 510000 Guangdong, P. R. China )

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Automatic infrared image recognition method

Abstract

Infrared image recognition plays an important role in the inspection of power equipment. Existing technologies dedicated to this purpose often require manually selected features, which are not transferable and interpretable, and have limited training data. To address these limitations, this paper proposes an automatic infrared image recognition framework, which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation. First, the features of an input image are extracted and embedded using a multi-head attention encoding–decoding mechanism. Thereafter, the embedded features are used to predict the equipment component category and location. In the located area, preliminary segmentation is performed. Finally, similar areas are gradually merged, and the temperature distribution of the equipment is obtained to identify a fault. Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and, hence, provides a good reference for the automation of power equipment inspection.

Keywords

Substation equipment, Infrared image intelligent recognition, Deep self-attention network, Multi-factor similarity calculation.

Fig.1 Recognition Model Structure Based on Infrared Images of the Equipment

Fig.2 Network Architecture of a Transformer

Fig.3 Equipment Temperature Identification Model

Fig.4 Flowchart of the Image Segmentation Algorithm

Fig.5 Infrared Images of Power Equipment in a Substation

Fig.6 Recognition Results of Power Equipment in a Substation Based on Infrared Images

Fig.7 Network Architecture of Faster RCNN

Fig.8 Network Architecture of SSD

本文引文信息

Li YC, Xu YP, Xu MK, et al. (2022) Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation. Global Energy Interconnection, 5(4): 397-408

李曜丞，许永鹏，胥明凯，等 (2022) 基于深度自注意力网络和多因素相似度计算的变电站设备红外图像自动识别方法. 全球能源互联网（英文）, 5(4): 397-408

Biographies

Yaocheng Li

Yaocheng Li received bachelor’s degree of Electrical Engineering from Southwest Jiaotong University, Chengdu, China, in 2018. He is currently pursuing Ph.D. degree at Shanghai Jiaotong University. His current research interest is computer-vision-based autonomous defect detection in power transmission equipment.

Yongpeng Xu

Yongpeng Xu received the Ph.D. degree from Shanghai Jiao Tong University, in 2019. In addition, from 2019 to 2021, he worked as a Post Doctoral Researcher in Shanghai Jiao Tong University. Currently, he is a assistant research fellow in the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai,China. His interests include condition monitoring, partial discharge and fault diagnosis.

Mingkai Xu

Mingkai Xu Senior engineer, graduated from Shandong University of technology with a bachelor’s degree in 1997, majoring in power system and automation. Now he works in State Grid Jinan power supply company, engaged in power grid operation, safety production and other management work. His main research interest is power system relay protection.

Siyuan Wang

Siyuan Wang Senior engineer, graduated from Shandong University with a master’s degree in 2006, majoring in power system and automation. Now he works in State Grid Jinan power supply company, engaged in the operation and maintenance management of substation electrical equipment. His main research interest is the operation and maintenance of primary equipment of power grid.

Zhicheng Xie

Zhicheng Xie received the B.Sc. degree in South China University of Technology, Guangzhou, China, in 2012 and Ph.D. degree in Huazhong University of Science and Technology, Wuhan, China, in 2017. His researches mainly focus on intelligent operation and maintenance technology for transformer and bushing.

Zhe Li

Zhe Li received the B.Sc., M.E. and Ph.D. degrees in high voltage and insulation technology at Shanghai Jiao Tong University, China in 2000, 2005 and 2007, respectively. He has been an associate professor in the Department of Electrical Engineering, Shanghai Jiao Tong University, and he was invited to Waseda University, Japan as a visiting researcher from 2008 to 2010. His interest is dielectric properties of polymer nano-composite and electrical insulation.

Xiuchen Jiang

Xiuchen Jiang received the B.E. degree in high voltage and insulation technology from Shanghai Jiao Tong University, Shanghai, China, in 1987, the M.S. degree in high voltage and insulation technology from Tsinghua University, Beijing, China, in 1992, and the Ph.D. degree in electric power system and automation from Shanghai Jiao Tong University in 2001. Currently, he is a Professor in the Shanghai Jiao Tong University. His research interests are electrical equipment online monitoring as well as condition-based maintenance and automation.

编辑：王彦博

审核：王伟

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Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation

上海交通大学 江秀臣等：基于深度自注意力网络和多因素相似度计算的变电站设备红外图像自动识别方法

郑重声明

上海交通大学江秀臣等：基于深度自注意力网络和多因素相似度计算的变电站设备红外图像自动识别方法