Component identification and defect detection in transmission lines based on deep learning

Issue title: Artificial Intelligence as a maturing and growing technology: An urgent need for intelligent systems

Guest editors: X. Yuan and M. Elhoseny

Article type: Research Article

Authors: Zheng, Xiangyu^{a; b; *} | Jia, Rong^{a; c} | Aisikaer, ^d | Gong, Linling^e | Zhang, Guangru^b | Dang, Jian^a

Affiliations: [a] School of Electrical Engineering, Xi’an University of Technology, Xi’an, Shaanxi, China | [b] State Grid Gansu Electric Power Research Institution, Lanzhou, Gansu, China | [c] Energy Intelligence Laboratory, Xi’an University of Technology, Xi’an, Shaanxi, China | [d] Xinjiang Goldwind Science & Technology Co., ltd, Urumqi, Xinjiang, China | [e] Lanzhou Petrochemical College of Vocational Technology, Lanzhou, Gansu, China

Correspondence: [*] Corresponding author. Xiangyu Zheng, E-mail: 1102zxy@163.com.

Abstract: Ensuring the stable and safe operation of the power system is an important work of the national power grid companies. The power grid company has established a special power inspection department to troubleshoot transmission line components and replace faulty components in a timely manner. At present, assisted manual inspection by drone inspection has become a trend of power line inspection. Automatically identifying component failures from images of UAV aerial transmission lines is a cutting-edge cross-cutting issue. Based on the above problems, the purpose of this article is to study the component identification and defect detection of transmission lines based on deep learning. This paper expands the dataset by adjusting the size of the convolution kernel of the CNN model and the rotation transformation of the image. The experimental results show that both methods can effectively improve the effectiveness and reliability of component identification and defect detection in transmission line inspection. The recognition and classification experiments were performed using the images collected by the drone. The experimental results show that the effectiveness and reliability of the deep learning method in the identification and defect detection of high-voltage transmission line components are very high. Faster R-CNN performs component identification and defect detection. The detection can reach a recognition speed of nearly 0.17 s per sheet, the recognition rate of the pressure-equalizing ring can reach 96.8%, and the mAP can reach 93.72%.

Keywords: Power line detection, deep learning, component recognition, faster R-CNN, network model

DOI: 10.3233/JIFS-189353

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 3147-3158, 2021