Abstract
With increasing global investment in offshore wind energy and rapid deployment of wind power technologies in deep water hazardous environments, the in-service inspection of wind turbines and their related infrastructure plays an important role in the safe and efficient operation of wind farm fleets. The use of unmanned aerial vehicle (UAV) and remotely piloted aircraft (RPA)—commonly known as “drones”—for remote inspection of wind energy infrastructure has received a great deal of attention in recent years. Drones have significant potential to reduce not only the number of times that personnel will need to travel to and climb up the wind turbines, but also the amount of heavy lifting equipment required to carry out the dangerous inspection works. Drones can also shorten the duration of downtime needed to detect defects and collect diagnostic information from the entire wind farm. Despite all these potential benefits, the drone-based inspection technology in the offshore wind industry is still at an early stage of development and its reliability has yet to be proven. Any unforeseen failure of the drone system during its mission may cause an interruption in inspection operations, and thereby, significant reduction in the electricity generated by wind turbines. In this paper, we propose a semiquantitative reliability analysis framework to identify and evaluate the criticality of mission failures—at both system and component levels—in inspection drones, with the goal of lowering the operation and maintenance (O&M) costs as well as improving personnel safety in offshore wind farms. Our framework is built based upon two well-established failure analysis methodologies, namely, fault tree analysis (FTA) and failure mode and effects analysis (FMEA). It is then tested and verified on a drone prototype, which was developed in the laboratory for taking aerial photography and video of both onshore and offshore wind turbines. The most significant failure modes and underlying root causes within the drone system are identified, and the effects of the failures on the system’s operation are analysed. Finally, some innovative solutions are proposed on how to minimize the risks associated with mission failures in inspection drones.
Highlights
In recent years, offshore wind energy technologies have gained widespread attention and experienced a rapid development because of the many advantages they offer
Significant investments have been made in recent years to deploy large-scale wind turbines of 9 to 12 megawatts (MW) in size, in order to achieve the best economies of scale in the offshore wind sector [2]
According to a research conducted on a 500-MW baseline offshore wind farm, the operation and maintenance (O&M) costs accounted for about 26% of the levelised cost of energy (LCoE) over the 25 years’ life of the project [3]
Summary
Offshore wind energy technologies have gained widespread attention and experienced a rapid development because of the many advantages they offer. Stout and Thompson [10] presented a five-stage process for visual inspection using drones in offshore wind farms These stages include creation of defect standard, Robotics 2021, 10, x FOR PEER REVIEW. The drones can capture high-quality images and videos from aerial views, which can be processed in command centres to identify where the defects on wind turbines are and what type of maintenance action should be performed to avoid failure occurrence. 3. Reliability Analysis Framework a reliability analysis and improvement framework based on two methodologies of FTA and FMEA is proposed to determine the criticality of mission failures in a drone-based inspection technology for offshore wind farms. Each of the possible failure modes would have an impact on the performance of the drone system, as well as perhaps on the wind turbine components being inspected and/or the health and safety of the personnel undertaking inspections. The total RPN of each component is calculated by adding the RPNs of all individuated failure modes
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