An Application of Fuzzy Fault Tree Analysis for Reliability Evaluation of Wind Energy System

References

• C. Liang, X. Wang, and Z. Dou, “Risk assessment and reliability analysis for wind turbine gearbox fault based on FFMECA,” 2017 4th Int. Conf. Syst. Informatics, ICSAI 2017, Vol. 2018-Janua, no. 20150203002, pp. 268–73, 2018.
   Google Scholar
• D. Zhou, G. Zhang, and F. Blaabjerg, “Optimal selection of power converter in DFIG wind turbine with enhanced system-level reliability,” IEEE Trans. Ind. Appl., Vol. 54, no. 4, pp. 3637–44, 2018. doi: 10.1109/TIA.2018.2822239
   Web of Science ®Google Scholar
• P. Liu, et al., “Design of wind turbine dynamic trip-off risk alarming mechanism for large-scale wind farms,” IEEE Trans. Sustain. Energy, Vol. 8, no. 4, pp. 1668–78, 2017. doi: 10.1109/TSTE.2017.2701348
   Web of Science ®Google Scholar
• H. Yang, K. Xie, H. M. Tai, and Y. Chai, “Wind farm layout optimization and its application to power system reliability analysis,” IEEE Trans. Power Syst, Vol. 31, no. 3, pp. 2135–43, 2016. doi: 10.1109/TPWRS.2015.2452920
   Web of Science ®Google Scholar
• O. Gomez, C. Portilla, and M. A. Rios, “Reliability analysis of substation monitoring systems based on branch PMUs,” IEEE Trans. Power Syst, Vol. 30, no. 2, pp. 962–9, 2015. doi: 10.1109/TPWRS.2014.2330736
   Web of Science ®Google Scholar
• M. H. Sahnoun, F. Bagui, M. Messaadia, M. H. Sahnoun, F. Bagui, and M. Messaadia, “Failure analysis of onshore wind farms based on experimental data,” in Mediterranean Conference on Information & Communication Technologies’ 2015, EL MOUSSATI Ali, Saidia, Morocco, May 2015, ffhal-01274496, 2016.
   Google Scholar
• M. AlMuhaini, and F. S. Al Badawi, “Reliability modelling and assessment of electric motor driven systems in hydrocarbon industries,” IET Electr. Power Appl, Vol. 9, no. 9, pp. 605–11, 2015. doi: 10.1049/iet-epa.2015.0089
   Web of Science ®Google Scholar
• K. Zou, A. P. Agalgaonkar, K. M. Muttaqi, and S. Perera, “An analytical approach for reliability evaluation of distribution systems containing dispatchable and nondispatchable renewable DG units,” IEEE Trans. Smart Grid, Vol. 5, no. 6, pp. 2657–65, 2014. doi: 10.1109/TSG.2014.2350505
   Web of Science ®Google Scholar
• V. S. Lopes, and C. L. T. Borges, “Impact of the combined integration of wind generation and small hydropower plants on the system reliability,” IEEE Trans. Sustain. Energy, Vol. 6, no. 3, pp. 1169–77, 2015. doi: 10.1109/TSTE.2014.2335895
   Web of Science ®Google Scholar
• S. Sulaeman, M. Benidris, J. Mitra, and C. Singh, “A wind farm reliability model considering both wind variability and turbine forced outages,” IEEE Trans. Sustain. Energy, Vol. 8, no. 2, pp. 629–37, 2017. doi: 10.1109/TSTE.2016.2614245
   Web of Science ®Google Scholar
• M. Nadjafi, M. A. Farsi, E. Zio, and A. K. Mousavi, “Fault trees analysis using expert opinion based on fuzzy-bathtub failure rates,” Qual. Reliab. Eng. Int, Vol. 34, no. 6, pp. 1142–57, 2018. doi: 10.1002/qre.2313
   Web of Science ®Google Scholar
• L. Wang, S. Li, O. Wei, M. Huang, and J. Hu, “An automated fault tree generation approach with fault configuration based on model checking,” IEEE Access., Vol. 6, pp. 46900–14, 2018. doi: 10.1109/ACCESS.2018.2863696
   Web of Science ®Google Scholar
• R. J. Rodriguez, “On qualitative analysis of fault trees using structurally persistent nets,” IEEE Trans. Syst. Man. Cybern. Syst, Vol. 46, no. 2, pp. 282–93, 2016. doi: 10.1109/TSMC.2015.2437360
   Web of Science ®Google Scholar
• X. Song, Z. Zhai, P. Zhu, and J. Han, “A stochastic computational approach for the analysis of fuzzy systems,” IEEE Access., Vol. 5, pp. 13465–77, 2017. doi: 10.1109/ACCESS.2017.2728123
   Web of Science ®Google Scholar
• Y. Mo, “A multiple-valued decision-diagram-based approach to solve dynamic fault trees,” IEEE Trans. Reliab, Vol. 63, no. 1, pp. 81–93, 2014. doi: 10.1109/TR.2014.2299674
   Web of Science ®Google Scholar
• L. Xing, B. A. Morrissette, and J. B. Dugan, “Combinatorial reliability analysis of imperfect coverage systems subject to functional dependence,” IEEE Trans. Reliab, Vol. 63, no. 1, pp. 367–82, 2014. doi: 10.1109/TR.2014.2299431
   Web of Science ®Google Scholar
• C. Joshi, F. Ruggeri, and S. P. Wilson, “Prior robustness for Bayesian Implementation of the fault tree analysis,” IEEE Trans. Reliab, Vol. 67, no. 1, pp. 170–83, 2018. doi: 10.1109/TR.2017.2778241
   Web of Science ®Google Scholar
• Y. Zhang, X. Han, B. Xu, M. Wang, P. Ye, and Y. Pei, “Risk-based admissibility analysis of wind power integration into power system with energy storage system,” IEEE Access., Vol. 6, pp. 57400–13, 2018. doi: 10.1109/ACCESS.2018.2870736
   Web of Science ®Google Scholar
• M. Gaied, A. M’Halla, and K. Ben Othmen, “Fuzzy diagnosis based on P-time petri nets for a winding machine,” 2017 Int. Conf. Control. Autom. Diagnosis, ICCAD 2017, pp. 1–6, 2017.
   Google Scholar
• H. Malik, and S. Mishra, “Application of fuzzy Q learning (FQL) technique to wind turbine imbalance fault identification using generator current signals,” in 2016 IEEE 7th Power India International Conference (PIICON), Bikaner, 2016, pp. 1–6.
   Google Scholar
• L. Tripathy, M. K. Jena, and S. R. Samantaray, “Decision tree-induced fuzzy rule-based differential relaying for transmission line including unified power flow controller and wind-farms,” IET Gener. Transm. Distrib, Vol. 8, no. 12, pp. 2144–52, 2014. doi: 10.1049/iet-gtd.2014.0023
   Web of Science ®Google Scholar
• J. Xiang, F. Machida, K. Tadano, and Y. Maeno, “An imperfect fault coverage model with coverage of irrelevant components,” IEEE Trans. Reliab, Vol. 64, no. 1, pp. 320–32, 2015. doi: 10.1109/TR.2014.2363155
   Web of Science ®Google Scholar
• L. Jin, C. Peng, and T. Jiang, “System-level electric field exposure assessment by the fault tree analysis,” IEEE Trans. Electromagn. Compat, Vol. 59, no. 4, pp. 1095–102, 2017. doi: 10.1109/TEMC.2017.2647961
   Web of Science ®Google Scholar
• D. M. E. Ingram, P. Schaub, D. A. Campbell, and R. R. Taylor, “Quantitative assessment of fault tolerant precision timing for electricity substations,” IEEE Trans. Instrum. Meas, Vol. 62, no. 10, pp. 2694–703, 2013. doi: 10.1109/TIM.2013.2263673
   Web of Science ®Google Scholar
• Y. Hiraoka, T. Murakami, K. Yamamoto, Y. Furukawa, and H. Sawada, “Method of computer-aided fault tree analysis for high-reliable and safety design,” IEEE Trans. Reliab, Vol. 65, no. 2, pp. 687–703, 2016. doi: 10.1109/TR.2015.2513050
   Web of Science ®Google Scholar
• M. Volk, S. Junges, and J. P. Katoen, “Fast dynamic fault tree analysis by model checking techniques,” IEEE Trans. Ind. Informatics, Vol. 14, no. 1, pp. 370–9, 2018. doi: 10.1109/TII.2017.2710316
   Web of Science ®Google Scholar
• S. Kabir, M. Yazdi, J. I. Aizpurua, and Y. Papadopoulos, “Uncertainty-aware dynamic reliability analysis framework for complex systems,” IEEE Access, Vol. 6, pp. 29499–515, 2018. doi: 10.1109/ACCESS.2018.2843166
   Web of Science ®Google Scholar
• Z. Zhou, and Q. Zhang, “Model event/fault trees with dynamic uncertain causality graph for better probabilistic safety assessment,” IEEE Trans. Reliab, Vol. 66, no. 1, pp. 178–88, 2017. doi: 10.1109/TR.2017.2647845
   Web of Science ®Google Scholar
• T. Peikert, H. Garbe, and S. Potthast, “Fuzzy-based risk analysis for IT-systems and their Infrastructure,” IEEE Trans. Electromagn. Compat, Vol. 59, no. 4, pp. 1294–301, 2017. doi: 10.1109/TEMC.2017.2682643
   Web of Science ®Google Scholar
• L. Xing, M. Tannous, V. M. Vokkarane, H. Wang, and J. Guo, “Reliability modeling of mesh storage area networks for internet of things,” IEEE Internet Things J, Vol. 4, no. 6, pp. 2047–57, 2017. doi: 10.1109/JIOT.2017.2749375
   Web of Science ®Google Scholar
• Y. Guo, H. Gao, and Q. Wu, “A combined reliability model of VSC-HVDC connected offshore wind farms considering wind speed correlation,” IEEE Trans. Sustain. Energy, Vol. 8, no. 4, pp. 1637–46, Oct. 2017. doi: 10.1109/TSTE.2017.2698442
   Web of Science ®Google Scholar
• X. Zheng, G. Zhou, J. Dai, H. Ren, and D. Li, “Drive system reliability analysis of wind turbine based on fuzzy fault tree,” Chinese Control Conf. CCC, Vol. 2016-Augus, pp. 6761–5, 2016.
   Google Scholar
• A. Mentes, and I. H. Helvacioglu, “An application of fuzzy fault tree analysis for spread mooring systems,” Ocean Eng., Vol. 38, no. 2–3, pp. 285–94, 2011. doi: 10.1016/j.oceaneng.2010.11.003
   Web of Science ®Google Scholar
• C. Saad, B. Mostafa, and H. Abderrahmane, “Performance analysis of faults detection in wind turbine generator based on high-resolution frequency estimation methods,” Int. J. Adv. Comput. Sci. Appl, Vol. 5, no. 4, pp. 139–48, 2014.
   Google Scholar
• W. Yang, P. J. Tavner, and M. Wilkinson, “Wind turbine condition monitoring and fault diagnosis using both mechanical and electrical signatures,” 2008 IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, pp. 1296–301, 2008. doi: 10.1109/AIM.2008.4601849
   Google Scholar
• B. Yao, H. Chen, X. Q. He, Q. Z. Xiao, and X. J. Kuang, “Reliability and failure analysis of DC/DC converter and case studies,” QR2MSE 2013 – Proc. 2013 Int. Conf. Qual. Reliab. Risk, Maintenance, Saf. Eng., pp. 1133–5, 2013. doi: 10.1109/QR2MSE.2013.6625766
   Google Scholar
• F. Kadri, S. Drid, F. Djeffal, and L. Chrifi-Alaoui, “Neural classification method in fault detection and diagnosis for voltage source inverter in variable speed drive with induction motor,” 2013 Eighth International Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER), Vol. 30, no. 1, pp. 1–5, 2013.
   Google Scholar
• S. Ozturk, V. Fthenakis, and S. Faulstich, “Failure modes, effects and criticality analysis for wind turbines considering climatic regions and comparing geared and direct drive wind turbines,” Energies, Vol. 11, no. 9, pp. 1–18, 2018. doi: 10.3390/en11092317
   Web of Science ®Google Scholar