Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (1): 103-113.doi: 10.23940/ijpe.21.01.p10.103113

• Orginal Article • Previous Articles     Next Articles

Optimal Model for Patrols of UAVs in Power Grid under Time Constraints

Caiming Zhanga, and Weina Fubc*   

  1. aCollege of Applied Technology, China University of Labor Relations, Beijing, 100000, China
    bCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, 010012, China
    cHunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * Corresponding author. E-mail address: fwn0124@yeah.net
  • Supported by:
    the Social Science Foundation of China [No.15BJY022], Researching Foundation of China University of Labor Relations [No15YY006], and Education Reforming Foundation of China University of Labor Relations [NoJGZX1404, ZYTS201806]

Abstract:

The safe operation of the power grid has a direct impact on the stability of the power supply with the power system. Due to the large number of equipment in the power grid, it is difficult for daily patrols. The efficiency of patrols of UAVs in power grids and the optimal configuration of UAVs are the purpose of the optimal model for patrols of UAVs in power grids under time constraints. Under time constraints, the optimization goal is to minimize the flight time of all UAVs or the longest flight time of a single UAV with the condition of completing all patrol tasks. It constructs the optimal model for patrol paths of UAVs in power grids through the space-time road network. The genetic algorithm is used to solve the optimal problem of patrol paths of UAVs. The results of scene simulation show that the patrol paths of UAVs in power grids planned by this model are more in line with actual needs and have obvious efficiency advantages.

Key words: time constraint, UAV, patrol in power grid, optimal model, time-space road network, genetic algorithm