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

Author:

Zheng Xiangyu12,Jia Rong13,Aisikaer 4,Gong Linling5,Zhang Guangru2,Dang Jian1

Affiliation:

1. School of Electrical Engineering, Xi’an University of Technology, Xi’an, Shaanxi, China

2. State Grid Gansu Electric Power Research Institution, Lanzhou, Gansu, China

3. Energy Intelligence Laboratory, Xi’an University of Technology, Xi’an, Shaanxi, China

4. Xinjiang Goldwind Science & Technology Co., ltd, Urumqi, Xinjiang, China

5. Lanzhou Petrochemical College of Vocational Technology, Lanzhou, Gansu, China

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%.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference35 articles.

1. Risk-constrained stochastic power procurement of storage-based large electricity consumer

2. Research on Location Selection Model of Distribution Network with Constrained Line Constraints Based on Genetic Algorithm;Guo;Neural Computing and Applications,2019

3. Fractional Transmission Line with Losses;Gómez-Aguilar;Zeitschrift Naturforschung Teil A,2015

4. Multiple travelling wave solutions for electrical transmission line model;Sardar;Nonlinear Dynamics,2015

5. New Pattern-Recognition Method for Fault Analysis in Transmission Line with UPFC;Moravej;IEEE Transactions on Power Delivery,2015

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3