无人机辅助智能电网故障终端数据采集优化策略

随着光伏等各类清洁能源的广泛使用，在移动边缘计算的支撑下，无人机经常被用于户外电网终端设备，特别是运行偏差故障终端的数据采集。然而，待采集的终端运行出现差错、终端数量大幅度增长以及无人机有限的能量和动态的飞行等问题，导致无人机难以快速获得待检测终端的准确位置。基于此，设计一种基于边缘计算的无人机辅助故障终端数据采集优化策略。通过构建基于随机分布的位置误差模型，研究一种无人机飞行轨迹和待采集终端设备的任务传输联合优化策略。联合利用 Bernstein 型不等式、凸优化和隐枚举法，构建高效的两阶段优化求解算法。仿真结果表明所提数据采集策略中无人机可以更加靠近户外的电网故障终端设备，数据采集的时间更长且准确率更高。

Optimal UAV assisted for failure terminal data collection strategy of smart grid

With the widespread use of various types of clean energy sources such as photovoltaic, supported by mobile edge computing technology, UAVs are always used to perform data collection for the outdoor power grid terminals. However, the substantial growing of the number of terminals to be collected and the limited energy and dynamic flight of UAVs, make it is difficult for UAVs to obtain the accurate locations of all terminals quickly. Therefore, this paper designed an optimal UAVassisted mass terminal data collection strategy based on edge computing. This work constructed a location error model based on random distribution, and designed a joint optimization strategy for the UAV flight trajectory and the task transmission of the grid devices to be collected. For the problem solution, it used the Bernstein-type inequalities to relax the variables to eliminate the influence caused by the probability distribution of device locations, and then solved the trajectory planning of the UAV by using continuous convex approximation. The simulation results show that under the proposed strategy, the UAV is closer to the smart grid failure terminal device, and the data collection time is longer and more accurate.