Posters

Abstract

The increasing size and scope of offshore wind farms drives the need for industry to reduce costs through more efficient daily operations, including inspection. Most recently autonomous vehicles have been investigated in conjunction with digital platform technologies to improve the efficiency and safety of inspection tasks. In this paper, we propose a drone swarm-vessel framework that combines autonomous vessels with a swarm of drones for the inspection of wind farms due to the lack of sufficient power of the drones to fly and communicate throughout missions. Once the drone's battery is depleted, no signals are transmitted and the communications become intermittent. The autonomous vessels will be the docking recharging station for the drone swarm. Furthermore, the fact that a 30-minute drone operation requires a 90-minute charging time complicates the scheduling task of maintaining the inspection as a continuous process. This sophisticated inspection framework requires an efficient routing and scheduling of drone swarm-vessels for the inspection of wind farms. We have developed a deterministic optimisation model and a multi-stage Bees metaheuristic to generate the optimal vessel routes to transport the drone swarms to the wind farm and schedule them to inspect the wind turbines. The multi-stage Bees metaheuristic involves the clustering to generate vessel's safety halting points, routing to generate the vessel routes, and forward pass scheduling to produce the start and finish times for each inspection task within an eight-hour time period. The Bees metaheuristic is sufficiently flexible to account for multiple autonomous vessels, drone swarm and shift profiles, while maintaining inspection unaffected by recharging processes. We have conducted several experiments utilising the 160 wind turbines dataset (Gwynt y Mor Wind farm at Wales) from the literature and randomly generated scenarios based on the number of autonomous vessels and drones used to conduct the inspection. The results are analysed to evaluate the performance of the proposed framework and multi-stage Bees metaheuristic to generate efficient routing and scheduling of drone swarm-vessels for the inspection of the offshore wind farms.