Research on automatic location and recognition of insulators in substation based on YOLOv3

被引:54
作者
Liu, Yunpeng [1 ]
Ji, Xinxin [1 ]
Pei, Shaotong [1 ]
Ma, Ziru [1 ]
Zhang, Gonghao [1 ]
Lin, Ying [2 ]
Chen, Yufeng [2 ]
机构
[1] North China Elect Power Univ, Hebei Prov Key Lab Power Transmiss Equipment Secu, Yonghua North St 619, Baoding City, Peoples R China
[2] State Grid Corp China Shandong Elect Power Res In, 1 South Second Ring Rd, Jinan, Peoples R China
基金
中国国家自然科学基金;
关键词
Electric power transmission networks - Light - Deep learning - Smart power grids;
D O I
10.1049/hve.2019.0091
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
With the development of a smart grid, the automatic location of power equipment is becoming a trend. In this study, a method for automatic location identification and diagnosis of external power insulation equipment based on YOLOv3 is proposed. This deep learning algorithm is used to extract the characteristics of image data under the visible light channel of the insulator. It learns and trains the collected data to realise the rapid location identification and frame selection of the external insulation equipment and extract discharge characteristics of the target box under the ultraviolet channel. According to the number of photons and the spot area information, the operating status of the equipment is determined. The results show that the YOLOv3 algorithm with a training rate of 0.005 achieved a fast convergence of the location recognition model. The average recognition accuracy was 88.7% and the average detection time was 0.0182 s. The combination of visible light path insulator target recognition and ultraviolet light path diagnosis can realise a lean and intelligent diagnosis of power equipment. This method had good real-time performance, accuracy, and robustness to the background. It provides a new concept for intelligent diagnosis and location analysis of power equipment.
引用
收藏
页码:62 / 68
页数:7
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