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Author:

Ma, Lei (Ma, Lei.) | Xu, Changfu (Xu, Changfu.) | Zuo, Guoyu (Zuo, Guoyu.) (Scholars:左国玉) | Bo, Bin (Bo, Bin.) | Tao, Fengbo (Tao, Fengbo.)

Indexed by:

EI Scopus

Abstract:

Insulators are the most common equipment in the power system, the failure of insulators will cause heavy economic loss to electric power companies, so it is very important to detect insulators effectively for inspecting their working states. This paper proposes a novel method to detect the insulators based on Faster R-CNN in which Region Proposal Network (RPN) is used to generate high-quality insulator candidates and the convolution features are shared with Fast R-CNN to detect the insulator. A large number of visible light images are used as experimental data in experiment, and the results show that this method can detect insulators in complex background with high precision as well as low time cost. © 2017 IEEE.

Keyword:

Electric losses Electric utilities Intelligent systems Losses Electric power system economics Light Convolutional neural networks Deep learning

Author Community:

  • [ 1 ] [Ma, Lei]Faculty of Information Technology, Beijing University of Technology, Beijing; 1000124, China
  • [ 2 ] [Xu, Changfu]State Grid Jiangsu Electric Power Company Research Institute, Nanjing; 211103, China
  • [ 3 ] [Zuo, Guoyu]Faculty of Information Technology, Beijing University of Technology, Beijing; 1000124, China
  • [ 4 ] [Bo, Bin]State Grid Jiangsu Electric Power Company Research Institute, Nanjing; 211103, China
  • [ 5 ] [Tao, Fengbo]State Grid Jiangsu Electric Power Company Research Institute, Nanjing; 211103, China

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Year: 2017

Page: 1410-1414

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 27

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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