1. Proceedings
  2. I3M
  3. 2022
  4. MAS
  5. 10.46354/i3m.2022.mas.010

Reliability simulation of a multi-state Wind Turbine
Generator using SHyFTOO

  • a Soheyl Moheb Khodayee ,
  • b Ludovica Oliveri,
  • c Jose Ignacio Aizpurua ,
  • d Diego D’Urso,
  • e Ferdinando Chiacchio 
  • a,b,e Dipartimento di Ingegneria Elettrica, Elettronica ed Informatica, Università degli Studi di Catania, Viale Andrea Doria 6, 95125 Catania, Italy.
  • c Electronics & Computing Department, Mondragon University, Goiru 2, 20500, Ar-rasate-Mondragon (Spain)
  • d Ikerbasque, Basque Foundation for Science, Euskadi Plaza 5, Bilbao (Spain)
Cite as
Khodayee S.M, Oliveri L., , Aizpurua J.I, D'Urso D., and Chiacchio F. (2022).,Reliability simulation of a multi-state Wind Turbine Generator using SHyFTOO. Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS 2022). , 010 . DOI: https://doi.org/10.46354/i3m.2022.mas.010

Abstract

Accurate reliability analysis modelling requires appropriate stochastic formalisms, which can capture the relevant operation and degradation characteristics of the system under analysis. Physical, working and environmental conditions can be very relevant, but their inclusions in a stochastic model is challenging. Among reliability analysis methodologies, a recent formalism named Hybrid Dynamic Fault Tree, which emerges from dynamic reliability theory, may be a suitable candidate to accomplish in this task. In this paper, a wind turbine generator case study has been chosen to demonstrate the potential capabilities of this modeling approach and motivate the use of this formalism to other practitioners. The main novelty of the proposed model is the integration of the wind speed, an independent exogenous physical variable, as a trigger to modify the parameters of the probability density of failures and the aging of components. The Hybrid Dynamic Fault Tree of the proposed case study has been coded using the SHyFTOO, an easy-to-use library developed under the MATLAB® framework. Achieved results show that the Hybrid Dynamic Fault Tree is a valid formalism that should be used to improve the modelling of a multi-state system when working and operative conditions cannot be disregarded.

References

  1. Ammar, M. et al. (2019) “Towards an Accurate Probabilistic Modeling and Statistical Analysis of Temporal Faults via Temporal Dynamic Fault-Trees (TDFTs),”IEEE Access, 7, pp. 29264–29276.Available at:https://doi.org/10.1109/ACCESS.2019.2902796.
  2. Arabian-Hoseynabadi, H., Tavner, P.J. and Oraee, H.(2010) “Reliability comparison of direct-drive andgeared-drive wind turbine concepts,” Wind Energy,13(1), pp.62–73. Available at:https://doi.org/10.1002/we.357.
  3. Arena, S., Roda, I. and Chiacchio, F. (2021) “Integratingmodelling of maintenance policies within astochastic hybrid automaton framework of dynamicreliability,” Applied Sciences (Switzerland), 11(5), pp. 1–18. Available at:https://doi.org/10.3390/app11052300.
  4. Aslansefat, K. and Latif-Shabgahi, G.R. (2019) “AHierarchical Approach for Dynamic Fault Trees
    Solution through Semi-Markov Process,” IEEETransactions on Reliability, 69(3), pp. 986–1003.Available at:https://doi.org/10.1109/TR.2019.2923893.
  5. Astolfi, D., Byrne, R. and Castellani, F. (2020) “Analysisof wind turbine aging through operation curves,”Energies, 13(21). Available at:https://doi.org/10.3390/en13215623.
  6. Babykina, G. et al. (2016) “Modeling and simulation ofa controlled steam generator in the context ofdynamic reliability using a Stochastic HybridAutomaton,” Reliability Engineering and SystemSafety, 152, pp. 115–136. Available at:https://doi.org/10.1016/j.ress.2016.03.009.
  7. Bouissou, M. (2007) “A generalization of DynamicFault Trees through Boolean logic Driven Markov Processes (BDMP)®,” in Proceedings of theEuropean Safety and Reliability Conference 2007,ESREL 2007-Risk, Reliability and Societal Safety,pp. 1051–1058.
  8. Byrne,R. et al. (2020) “A study of wind turbineperformance decline with age through operation
    data analysis,” Energies, 13(8). Available at:https://doi.org/10.3390/en13082086.
  9. Calixto, E. (2013) “Chapter 6-Reliability and SafetyProcesses BT-Gas and Oil Reliability Engineering,”in Gas and Oil Reliability Engineering, pp. 421–496.
    Available at:http://www.sciencedirect.com/science/article/pii/B978012391914400006X (Accessed: October 6, 2021).
  10. Čepin, M. and Mavko, B. (2002) “A dynamic fault tree,”ReliabilityEngineering and System Safety, 75(1), pp.
    83–91. Available at: https://doi.org/10.1016/S0951-8320(01)00121-1.
  11. Chiacchio, F. et al. (2011) “Dynamic fault trees resolution: A conscious trade-off betweenanalytical and simulative approaches,” ReliabilityEngineering and System Safety, 96(11), pp. 1515–1526. Available at:https://doi.org/10.1016/j.ress.2011.06.014.
  12. Chiacchio, F. et al. (2016) “SHyFTA, a Stochastic HybridFault Tree Automaton for the modelling and simulation of dynamic reliability problems,” Expert Systems with Applications, 47, pp. 42–57. A vailable at: https://doi.org/10.1016/j.eswa.2015.10.046.
  13. Chiacchio, F., Iacono, A., et al. (2020) “A general framework for dependability modelling coupling discrete-event and time-driven simulation, ”Reliability Engineering and System Safety, 199.Available at:https://doi.org/10.1016/j.ress.2020.106904.
  14. Chiacchio, F., Aizpurua, J.I., et al.(2020) “SHyFTOO, anobject-oriented Monte Carlo simulation library forthe modeling of Stochastic Hybrid Fault TreeAutomaton,” Expert Systems with Applications,146. Available at:https://doi.org/10.1016/j.eswa.2019.113139.
  15. Codetta-Raiteri, D. (2011) “Integrating severalformalisms in order to increase fault trees modeling power,” Reliability Engineering and System Safety,96(5), pp. 534–544. Available at:https://doi.org/10.1016/j.ress.2010.12.027.
  16. Dupont, E., Koppelaar, R. and Jeanmart, H. (2018)“Global available wind energy with physical andenergy return on investment constraints,” AppliedEnergy, 209, pp. 322–338. Available at:https://doi.org/10.1016/j.apenergy.2017.09.085.
  17. Durga Rao, K. et al. (2009) “Dynamic fault tree analysisusing Monte Carlo simulation in probabilistic safety
    assessment,” Reliability Engineering and SystemSafety, 94(4), pp. 872–883. Available at:https://doi.org/10.1016/j.ress.2008.09.007.
  18. Khodayee, S.M., Chiacchio, F. and Papadopoulos, Y.(2021) “A novel approach based on stochastic hybridfault tree to compare alternative flare gas recoverysystems,” IEEE Access, 9, pp. 51029–51049.Availableat:https://doi.org/10.1109/ACCESS.2021.3069807.
  19. Merle, G. et al. (2010) “Probabilistic algebraic analysisof fault trees with priority dynamic gates and repeated events,” IEEE Transactions on Reliability, 59(1), pp. 250–261. Available at:https://doi.org/10.1109/TR.2009.2035793.
  20. Merle, G., Roussel, J.M. and Lesage, J.J. (2011) “Dynamicfault tree analysis based on the structure function,”in Proceedings-Annual Reliability andMaintainability Symposium. Available at:https://doi.org/10.1109/RAMS.2011.5754452.
  21. Raiteri, D.C. et al. (2004) “Repairable Fault Tree for theautomatic evaluation of repair policies,” inProceedings of the International Conference on Dependable Systems and Networks, pp. 659–668.Available at:https://doi.org/10.1109/dsn.2004.1311936.
  22. Su,C. and Fu, Y.Q. (2014a) “Reliability assessment forwind turbines considering the influence of windspeed using bayesian network,” Eksploatacja iNiezawodnosc, 16(1), pp. 1–8.
  23. Su, C. and Fu, Y.Q. (2014b) “Reliability assessment forwind turbines considering the influence of windspeed using bayesian network,” Eksploatacja iNiezawodnosc, 16(1), pp. 1–8. Available at:https://www.infona.pl/resource/bwmeta1.element.baztech-56b87a18-1bde-4fab-a434-4d0ac2022a63 (Accessed: July 20, 2022).
  24. Yang, L. et al. (2021)“Operations & maintenanceoptimization of wind turbines integrating wind andaging information,” IEEE Transactions onSustainable Energy, 12(1), pp. 211–221. Available at:https://doi.org/10.1109/TSTE.2020.2986586.
  25. Yuge, T., Tamura, N. and Yanagi, S. (2012) “Repairablefault tree analysis using renewal intensities,” inQuality Technology and Quantitative Management,pp. 231–241. Available at:https://doi.org/10.1080/16843703.2012.11673289.