Pick-up & Deliver in Maintenance Management of Renewable Energy Power Plants

• Autorzy: Vincenza Carchiolo , Francesco Di Dio , Longheu Alessandro , Malgeri Michele , Mangioni Giuseppe , Romeo Antonio , Trapani Natalia

Identyfikatory

DOI

10.15439/2020F114

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Logistic optimization is a strategic element in many industrial processes, given that an optimized logistics makes the processes more efficient. A relevant case, in which the optimization of logistics can be decisive, is the maintenance in a Wind Farm where it can lead directly to a saving of energy cost. Wind farm maintenance presents, in fact, significant logistical challenges. They are usually distributed throughout the territory and also located at considerable distances from each other, they are generally found in places far from uninhabited centers and sometimes difficult to reach and finally spare parts are rarely available on the site of the plant itself. In this paper, we will study the problem concerning the optimization of maintenance logistics of wind plants based on the use of specific vehicle routing optimization algorithms. In particular a pickup-and- delivery algorithm with time-window is adopted to satisfy the maintenance requests of these plants, reducing their management costs. The solution was applied to a case study in a renewable energy power plant. Results time reduction and simplification and optimization obtained in the real case are discussed to evaluate the effectiveness and efficiency of the adopted approach.

Słowa kluczowe

EN

logistics; maintenance engineering; optimisation; renewable energy sources; vehicle routing; wind power plants

PL

logistyka; inżynieria utrzymania ruchu; optymalizacja; odnawialne źródła energii; wyznaczanie tras pojazdów; elektrownia wiatrowa

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 579--585
• Opis fizyczny: Bibliogr. 22 poz., wz., rys., tab.

Twórcy

autor

Vincenza Carchiolo

• Dipartimento di Matematica ed Informatica - Universitá di Catania - Catania - Italy

autor

Francesco Di Dio

• Development and Support Center - BaxEnergy - Catania, Italy

autor

Longheu Alessandro

• Dipartimento di Ingegneria Elettrica Elettronica Informatica - Universitá di Catania - Catania - Italy

autor

Malgeri Michele

• Dipartimento di Ingegneria Elettrica Elettronica Informatica - Universitá di Catania - Catania - Italy

autor

Mangioni Giuseppe

• Dipartimento di Ingegneria Elettrica Elettronica Informatica - Universitá di Catania - Catania - Italy

autor

Romeo Antonio

• Dipartimento di Ingegneria Elettrica Elettronica Informatica - Universitá di Catania - Catania - Italy

autor

Trapani Natalia

• Dipartimento di Ingegneria Elettrica Elettronica Informatica - Universitá di Catania - Catania - Italy

Bibliografia

• 1. F. Castellani, D. Astolfi, P. Sdringola, S. Proietti, and L. Terzi, “Analyzing wind turbine directional behavior: Scada data mining techniques for efficiency and power assessment,” Applied Energy, vol. 185, pp. 1076 – 1086, 2017. http://dx.doi.org/10.1016/j.apenergy.2015.12.049 Clean, Efficient and Affordable Energy for a Sustainable Future. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0306261915016220
• 2. X. Jin, Z. Xu, and W. Qiao, “Condition monitoring of wind turbine generators using SCADA data analysis,” IEEE Transactions on Sustainable Energy, pp. 1–1, 2020. http://dx.doi.org/10.1109/TSTE.2020.2989220
• 3. L. Chen, G. Xu, Q. Zhang, and X. Zhang, “Learning deep representation of imbalanced scada data for fault detection of wind turbines,” Measurement, vol. 139, pp. 370 – 379, 2019. http://dx.doi.org/10.1016/j.measurement.2019.03.029. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0263224119302386
• 4. J. Dai, W. Yang, J. Cao, D. Liu, and X. Long, “Ageing assessment of a wind turbine over time by interpreting wind farm scada data,” Renewable Energy, vol. 116, pp. 199 – 208, 2018. http://dx.doi.org/10.1016/j.renene.2017.03.097 Real-time monitoring, prognosis and resilient control for wind energy systems. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0960148117302896
• 5. P. B. Dao, W. J. Staszewski, T. Barszcz, and T. Uhl, “Condition monitoring and fault detection in wind turbines based on cointegration analysis of scada data,” Renewable Energy, vol. 116, pp. 107 – 122, 2018. http://dx.doi.org/10.1016/j.renene.2017.06.089 Real-time monitoring, prognosis and resilient control for wind energy systems. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0960148117305931
• 6. P. Bangalore and M. Patriksson, “Analysis of scada data for early fault detection, with application to the maintenance management of wind turbines,” Renewable Energy, vol. 115, pp. 521–532, 2018. http://dx.doi.org/10.1016/j.renene.2017.08.073. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0960148117308340
• 7. D. Astolfi, F. Castellani, and F. Natili, “Wind turbine generator slip ring damage detection through temperature data analysis,” Diagnostyka, vol. 20, no. 3, pp. 3–9, 2019. [Online]. Available: http://dx.doi.org/10.29354/diag/109968
• 8. C. Sequeira, A. Pacheco, P. Galego, and E. Gorbena, “Analysis of the efficiency of wind turbine gearboxes using the temperature variable,” Renewable Energy, vol. 135, no. C, pp. 465–472, 2019. http://dx.doi.org/10.1016/j.renene.2018.12. [Online]. Available: https://ideas.repec.org/a/eee/renene/v135y2019icp465-472.html
• 9. Y. Qiu, Y. Feng, J. Sun, W. Zhang, and D. Infield, “Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data,” IET Renewable Power Generation, vol. 10, no. 5, pp. 661–668, 2016. http://dx.doi.org/10.1049/iet-rpg.2015.0160
• 10. E. Máximo and V. Pinheiro, “XMILE - an expert system for maintenance learning from textual reports (S),” in The 30th International Conference on Software Engineering and Knowledge Engineering, Hotel Pullman, Redwood City, California, USA, July 1-3, 2018, Ó. M. Pereira, Ed. KSI Research Inc. and Knowledge Systems Institute Graduate School, 2018. pp. 492–491. [Online]. Available: 10.18293/SEKE2018-197
• 11. C. Bertero, M. Roy, C. Sauvanaud, and G. Tredan, “Experience report: Log mining using natural language processing and application to anomaly detection,” in 2017 IEEE 28th International Symposium on Software Reliability Engineering (ISSRE), 2017. http://dx.doi.org/10.1109/ISSRE.2017.43 pp. 351–360.
• 12. V. Carchiolo., A. Longheu., V. di Martino., and N. Consoli., “Power plants failure reports analysis for predictive maintenance,” in Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,, INSTICC. SciTePress, 2019. http://dx.doi.org/10.5220/0008388204040410. ISBN 978-989-758-386-5 pp. 404–410.
• 13. V. Carchiolo, G. Catalano, M. Malgeri, C. Pellegrino, G. Platania, and N. Trapani, “Modelling and optimization of wind farms’ processes using BPM,” in Information Technology for Management: Current Research and Future Directions, E. Ziemba, Ed. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43353-6_6. ISBN 978-3-030-43353-6 pp. 95–115.
• 14. M. Shafiee and J. D. SÞrensen, “Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies,” Reliability Engineering & System Safety, vol. 192, p. 105993, 2019. http://dx.doi.org/10.1016/j.ress.2017.10.025 Complex Systems RAMS Optimization: Methods and Applications. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S095183201630789X
• 15. Y. Dalgic, I. Lazakis, I. Dinwoodie, D. McMillan, and M. Revie, “Advanced logistics planning for offshore wind farm operation and maintenance activities,” Ocean Engineering, vol. 101, pp. 211–226, 2015. http://dx.doi.org/10.1016/j.oceaneng.2015.04.040. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0029801815001213
• 16. S. Jbili, A. Chelbi, M. Radhoui, and M. Kessentini, “Integrated strategy of vehicle routing and maintenance,” Reliability Engineering & System Safety, vol. 170, pp. 202 – 214, 2018. http://dx.doi.org/10.1016/j.ress.2017.09.030. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0951832017303174
• 17. D. Fan, Y. Ren, Q. Feng, B. Zhu, Y. Liu, and Z. Wang, “A hybrid heuristic optimization of maintenance routing and scheduling for offshore wind farms,” Journal of Loss Prevention in the Process Industries, vol. 62, p. 103949, 2019. http://dx.doi.org/10.1016/j.jlp.2019.103949. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0950423019304462
• 18. C. A. Irawan, M. Eskandarpour, D. Ouelhadj, and D. Jones, “Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm,” European Journal of Operational Research, 2019. http://dx.doi.org/10.1016/j.ejor.2019.08.032. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0377221719307027
• 19. Y. T. Sasmi Hidayatul, A. Djunaidy, and A. Muklason, “Solving multi-objective vehicle routing problem using hyper-heuristic method by considering balance of route distances,” in 2019 International Conference on Information and Communications Technology (ICOIACT), 2019. http://dx.doi.org/10.1109/ICOIACT46704.2019.8938484 pp. 937–942.
• 20. N. Y. Yurusen, P. N. Rowley, S. J. Watson, and J. J. Melero, “Automated wind turbine maintenance scheduling,” Reliability Engineering & System Safety, vol. 200, p. 106965, 2020. http://dx.doi.org/10.1016/j.ress.2020.106965. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0951832019306209
• 21. A. Froger, M. Gendreau, J. E. Mendoza, E. Pinson, and L.-M. Rousseau, “A branch-and-check approach for a wind turbine maintenance scheduling problem,” Computers & Operations Research, vol. 88, pp. 117 – 136, 2017. http://dx.doi.org/10.1016/j.cor.2017.07.001. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S030505481730165X
• 22. R. Bent and P. V. Hentenryck, “A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows,” Computers & Operations Research, vol. 33, no. 4, pp. 875 – 893, 2006. http://dx.doi.org/10.1016/j.cor.2004.08.001 Part Special Issue: Optimization Days 2003. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0305054804001911

Uwagi

1. This work was partially supported by WEAMS N.F/050145/00/X32 project

2. Track 4: Information Systems and Technology

3. Technical Session: 15th Conference on Information Systems Management

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).