Open AccessUAV Inspection Path Planning Based on Transmission Line Technology
Author(s) -
Wuneng Liu,
Lilong Liu,
Gengli He,
Peng Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1648/4/042083
Subject(s) - workload , electric power transmission , transmission (telecommunications) , computer science , transmission line , path (computing) , fault detection and isolation , line (geometry) , work (physics) , reliability engineering , power transmission , real time computing , power (physics) , engineering , telecommunications , artificial intelligence , electrical engineering , geometry , mathematics , mechanical engineering , physics , quantum mechanics , actuator , programming language , operating system
With the continuous progress of human society and the rapid development of economy, the power industry closely related to human production and life is also developing rapidly. Electric power industry is also playing an increasingly important role in the development of national economy. Subsequently, transmission security becomes more and more important. However, at present, the traditional single manual line inspection has the characteristics of high labor intensity, high safety risk, high safety risk, low operation efficiency, and many deficiencies seriously restrict the intelligent transformation of transmission line operation and maintenance. Therefore, this paper proposes a new transmission line detection scheme based on unmanned aerial vehicle (UAV) detection path planning. Transmission line detection system based on UAV and ground monitoring equipment basically meets the requirements of transmission line detection. It is found that the UAV based transmission line inspection work arrangement efficiency is higher, and the fault detection rate is higher. The accuracy rate of UAV line inspection method proposed in this paper reaches 93.7%, which can effectively reduce the workload of staff and improve the work efficiency.