MEC-Driven UAV-Enabled Routine Inspection Scheme in Wind Farm Under Wind Influence

Abstract

As a promising choice of alternative energy, wind power will account for a major part of energy generation in future Energy Internet. With the exploitation of wind power, multiple wind turbines (WTs) are deployed at remote and harsh areas, in which the adverse working environment may lead to enormous WT operating and maintenance costs. Deploying unmanned aerial vehicles (UAVs) for WT detection and sensory data processing in wind farms has been considered as a promising technology to reduce the costs and improve inspection efficiency. In this paper, a mobile edge computing (MEC) driven UAV routine inspection scheme is proposed, in which the UAV not only detects WTs in multiple sorties, but also provides computing and offloading services. To provide seamless communication service, UAV can offload the sensory data to the ground station or satellite optimally. In order to minimize the total completion time, we jointly optimize the UAV trajectory and computation operations, while guaranteeing the data processing accuracy. In the proposed scheme, in order to overcome the influence of wind on UAV trajectory planning, a low complexity WT routine inspection trajectory and UAV scheduling approach is designed firstly. Then, we present an iterative optimization solution to minimize the energy consumption of computation processing, via finding the optimal offloading trajectory and computation offloading parameters. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of UAV routine inspection system performance.

Keywords

• Energy Internet
• mobile edge computing
• wind turbine
• unmanned aerial vehicle
• task offloading