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

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

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

Content Provider	IET Digital Library
Author	Liu, Yunpeng Ji, Xinxin Pei, Shaotong Ma, Ziru Zhang, Gonghao Lin, Ying Chen, Yufeng
Abstract	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.
Starting Page	62
Ending Page	68
Page Count	7
Volume Number	5
e-ISSN	23977264
Issue Number	Issue 1, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/hve/5/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/hve.2019.0091
Journal	High Voltage
Publisher	The Institution of Engineering and Technology CEPRI
Publisher Date	2019-10-08
Access Restriction	Open
Rights License	Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Automatic Location Identification Computer Vision And Image Processing Technique Connector Deep Learning Algorithm Discharge Characteristic External Insulation Equipment External Power Insulation Equipment Fault Diagnosis Feature Extraction Frame Selection Image Data Image Recognition Insulator Intelligent Diagnosis Knowledge Engineering Technique Lean Diagnosis Learning in AI Light Path Diagnosis Location Analysis Location Recognition Model Power Apparatus Power Engineering Computing Power Equipment Power Line Supports Rapid Location Identification Smart Grid Smart Power Grid Substation Substations Time 0.0182 S Training Rate Ultraviolet Channel Visible Light Channel Visible Light Path Insulator Target Recognition YOLOv3 Algorithm
Content Type	Text
Resource Type	Article

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