Proactive Mission Planning of Unmanned Aerial Vehicle Fleets Used in Offshore Wind Farm Maintenance
Source abstract
Abstract
This paper presents a declarative model of maintenance logistics for offshore wind farms. Its implementation in decision-making tools supporting wind turbine maintenance enables online prototyping of alternative scenarios and variants of wind turbine servicing, including weather-related operation vessel movement and routing of unmanned aerial vehicle (UAV) fleets carrying out maintenance on these wind turbines during monitoring or component-delivery missions. The possibility of implementing the model was verified via two case studies focusing, separately, on the issues of routing and scheduling of a UAV fleet used for the inspection of wind turbines and the distribution of ordered spare parts. The open structure of the model allows for its easy generalization, expanding the range of supported functions, including vessel fleet routing in an offshore wind farm, staff and competence planning of service teams, and supply chain management, enabling the planning of tool sets distributed to serviced wind turbines. Computer experiments conducted for various weather conditions confirm the competitiveness of the proposed approach.
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Research context
OpenAlex topics: Vehicle Routing Optimization Methods · Advanced Manufacturing and Logistics Optimization · Optimization and Packing Problems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electronics and Computer Science, Koszalin University of Technology, 75-453 Koszalin, Poland
Department of Materials and Production, Aalborg University, 9220 Aalborg, Denmark
References (40)
Show source-provided references
1. Maintenance logistics organization for offshore wind energy: Current progress and future perspectives10.1016/j.renene.2014.11.045 ↗
2. Recent advances and future trends on maintenance strategies and optimisation solution techniques for offshore sector10.1016/j.oceaneng.2022.110986 ↗
3. Spare Parts Planning for Offshore Wind Turbines Subject to Restrictive Maintenance Conditions10.1016/j.procir.2013.06.033 ↗
4. Rajabi, M.S., Beigi, P., and Aghakhani, S. (2022). Handbook of Smart Energy Systems, Springer.
5. Shafiee, M., Zhou, Z., Mei, L., Dinmohammadi, F., Karama, J., and Flynn, D. (2021). Unmanned Aerial Drones for Inspection of Offshore Wind Turbines: A Mission-Critical Failure Analysis. Robotics, 10.10.3390/robotics10010026 ↗
6. Thermographic non-destructive inspection of wind turbine blades using unmanned aerial systems10.1179/1743289815Y.0000000003 ↗
7. Nordin, M.H., Sharma, S., Khan, A., Gianni, M., Rajendran, S., and Sutton, R. (2022). Collaborative Unmanned Vehicles for Inspection, Maintenance; Repairs of Offshore Wind Turbines. Drones, 6.10.3390/drones6060137 ↗
8. Yu, X. (2019). Modelling Offshore Wind Farm Operation and Maintenance: The Benefits of Condition Monitoring, Cambridge Scholars Publishing.
9. Costa, Á.M., Orosa, J.A., Vergara, D., and Fernández-Arias, P. (2021). New Tendencies in Wind Energy Operation and Maintenance. Appl. Sci., 11.10.3390/app11041386 ↗
10. The AEROARMS Project: Aerial Robots with Advanced Manipulation Capabilities for Inspection and Maintenance10.1109/MRA.2018.2852789 ↗
11. Exchange of effectors for small mobile robots and UAV10.1007/978-3-030-00232-9_32 ↗
12. Endrerud, O.E.V., Liyanage, J.P., and Keseric, N. (2015, January 6–9). Marine Logistics Decision Support for Operation and Maintenance of Offshore Wind Parks with a Multi Method Simulation Model. Proceedings of the Winter Simulation Conference, Huntington Beach, CA, USA.10.1109/WSC.2014.7020021 ↗
13. Routing and Scheduling of Maintenance Fleet for Offshore Wind Farms10.1260/0309-524X.39.1.15 ↗
14. Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data10.1109/ACCESS.2021.3132684 ↗
15. Pandit, R.K., Astolfi, D., and Cardenas, I.D. (2023). A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines. Energies, 16.10.3390/en16041654 ↗
16. Service operation vessels for offshore wind farm maintenance: Optimal stock levels10.1016/j.rser.2021.111158 ↗
17. A dynamic programming-based maintenance model of offshore wind turbine considering logistic delay and weather condition10.1016/j.ress.2019.106512 ↗
18. Radzki, G., Bocewicz, G., and Banaszak, Z. (2023). Proactive-Reactive Approach to Disruption-Driven UAV Routing Problem, Springer.10.1007/978-3-031-25844-2_5 ↗
19. Thibbotuwawa, A., Bocewicz, G., Radzki, G., Nielsen, P., and Banaszak, Z. (2020). UAV mission planning resistant to weather uncertainty. Sensors, 20.10.3390/s20020515 ↗
20. Ground-vehicle and unmanned-aerial-vehicle routing problems from two-echelon scheme perspective: A review10.1016/j.ejor.2021.02.022 ↗
21. Drone delivery from trucks: Drone scheduling for given truck routes10.1002/net.21847 ↗
22. Campbell, J.F., Sweeney, D.C., and Zhang, J. (2023, July 03). Strategic Design for Delivery with Trucks and Drones. Available online: https://www.researchgate.net/publication/316169890_Strategic_Design_for_Delivery_with_Trucks_and_Drones.
23. Optimal delivery routing with wider drone-delivery areas along a shorter truck-route10.1016/j.eswa.2018.03.032 ↗
24. Synchronized Truck and Drone Routing in Package Delivery Logistics10.1109/TITS.2020.2992549 ↗
25. A declarative modelling framework for routing of multiple UAVs in a system with mobile battery swapping stations10.1007/978-3-319-97490-3_42 ↗
26. A compilation of UAV applications for precision agriculture10.1016/j.comnet.2020.107148 ↗
27. Nikhil, N., Shreyas, S.M., Vyshnavi, G., and Yadav, S. (2020, January 20–22). Unmanned Aerial Vehicles (UAV) in Disaster Management Applications. Proceedings of the 3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020, Tirunelveli, India.10.1109/ICSSIT48917.2020.9214241 ↗
28. Savva, A., Zacharia, A., Makrigiorgis, R., Anastasiou, A., Kyrkou, C., Kolios, P., Panayiotou, C., and Theocharides, T. (2021, January 15–18). ICARUS: Automatic Autonomous Power Infrastructure Inspection with UAVs. Proceedings of the 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021, Athens, Greece.10.1109/ICUAS51884.2021.9476742 ↗
29. Corrigendum: The Fourth-Party Logistics Routing Problem Using Ant Colony System-Improved Grey Wolf Optimization10.1155/2022/9864064 ↗
30. Ni, J., Tang, J., and Wang, R. (2022). Hybrid Algorithm of Improved Beetle Antenna Search and Artificial Fish Swarm. Appl. Sci., 12.10.3390/app122413044 ↗
31. Hou, Y., Gao, H., Wang, Z., and Du, C. (2022). Improved Grey Wolf Optimization Algorithm and Application. Sensors, 22.10.3390/s22103810 ↗
32. Hao, Z., Zhang, H., and Zhang, Y. (2023). Stock Portfolio Management by Using Fuzzy Ensemble Deep Reinforcement Learning Algorithm. J. Risk Financ. Manag., 16.10.3390/jrfm16030201 ↗
33. Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry10.1007/s42452-021-04576-7 ↗
34. Tian, D.P., and Li, N.Q. (2009, January 11–17). Fuzzy Particle Swarm Optimization Algorithm. Proceedings of the IJCAI International Joint Conference on Artificial Intelligence, Pasadena, CA, USA.10.1109/JCAI.2009.50 ↗
35. Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development10.1016/j.eswa.2020.113856 ↗
36. Wang, Y., Huang, L., and Guo, Y. (2013, January 12–15). A Declarative Approach for Modeling Logistics Service Processes. Proceedings of the 2nd International Conference on Logistics, Informatics and Service Science, Beijing, China.
37. Offshore wind turbine operations and maintenance: A state-of-the-art review10.1016/j.rser.2021.110886 ↗
38. Grzegorz, R., Grzegorz, B., Bogdan, D., and Zbigniew, B. (2021). Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling. Appl. Sci., 11.10.3390/app11198898 ↗
39. Radzki, G., Nielsen, I., Golińska-dawson, P., Bocewicz, G., and Banaszak, Z. (2021). Reactive UAV fleet’s mission planning in highly dynamic and unpredictable environments. Sustainability, 13.10.3390/su13095228 ↗
40. Radzki, G., Relich, M., Bocewicz, G., and Banaszak, Z. (2022). Distributed Computing and Artificial Intelligence, Springer.