|Authors||H. Chung, S. Maharjan, Y. Zhang, F. Eliassen and T. Yuan|
|Title||Edge Intelligence Empowered UAV s for Automated Wind Farm Monitoring in Smart Grids|
|Project(s)||The Center for Resilient Networks and Applications, Simula Metropolitan Center for Digital Engineering|
|Publication Type||Proceedings, refereed|
|Year of Publication||2020|
|Conference Name||IEEE Globecom 2020|
With the exploitation of wind power, more turbines will be deployed at remote areas possibly with harsh working conditions (e.g., offshore wind farm). The adverse working environment may lead to massive operating and maintenance costs of turbines. Deploying unmanned aerial vehicles (UAVs) for turbine inspection is considered as a viable alternative to manual inspections. An important objective of automated UAV inspection is to minimize the flight time of the UAVs to inspect all the turbines. A first contribution of this paper is thus formulating an optimization problem to compute the optimal routes for turbine inspection satisfying the above goal. On the other hand, the limited computational capability on UAVs can be used to increase the power generation of wind turbine. Power generation from the turbines can be optimized by controlling the yaw angle of the turbines. Forecasting wind conditions such as wind speed and wind direction is crucial for solving both optimization problems. Therefore, UAVs can utilize their limited computational capability to perform wind forecasting. In this way, UAVs form edge intelligence in offshore wind farm. With the forecasted wind conditions, we design two algorithms to solve the formulated problems, and then evaluate the proposed methods with real-world data. The results reveal that the proposed methods offer an improvement of 44% of the power generation from the turbine compared to hour-ahead forecasting and 25% reduction of the flight time of the UAVs compared to the chosen baseline method.