Open Access
Issue |
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
|
|
---|---|---|
Article Number | 03003 | |
Number of page(s) | 8 | |
Section | Wind Turbine and Energy Systems | |
DOI | https://doi.org/10.1051/e3sconf/202454003003 | |
Published online | 21 June 2024 |
- Y. Wolde-Rufael, “Energy consumption and economic growth: the experience of African countries revisited.” Energy Economics, 31(2), 217–224, 2009. https://doi.org/10.1016/j.eneco.2008.11.005 [Google Scholar]
- M. Deveci, E. Özcan, R. John, D. Pamucar, H. Karaman, Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS, Applied Soft Computing, Vol. 109,107532, 2021. https://doi.org/10.1016/j.asoc.2021.107532 [Google Scholar]
- P. Zhou, P.T. Yin, “An opportunistic condition-based maintenance strategy for offshore wind farm based on predictive analytics”, Renewable and Sustainable Energy Reviews, Vol.109, 1–9, 2019, https://doi.org/10.1016/j.rser.2019.03.049 [Google Scholar]
- M. Wilkinson, et al. “Comparison of methods for wind turbine condition monitoring with SCADA data”, IET Renewable Power Generation,vol.8, no. 4, pp- 390–397 2014. https://doi.org/10.1049/iet-rpg.2013.0318 [Google Scholar]
- A. Kuhnle, J. Jakubik, & G. Lanza, “Reinforcement learning for opportunistic maintenance optimization”, Prod. Eng. Res. Devel. 13, 33–41 (2019). https://doi.org/10.1007/s11740-018-0855-7 [Google Scholar]
- M. Garan, K. Tidriri, and I. Kovalenko, “A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines,” Energies, vol. 15, no. 3, p. 826, Jan. 2022, doi: 10.3390/en15030826. [CrossRef] [Google Scholar]
- G. K. Durbhaka and B. Selvaraj, “Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach,” 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016, pp. 1839–1842, doi: 10.1109/ICACCI.2016.7732316. [Google Scholar]
- Schlechtingen, M.; Santos, I.F. Condition Monitoring with Ordinary Wind Turbine SCADA Data—A Neuro-Fuzzy Approach. Proceedings of the European Wind Energy Association (EWEA) Offshore 2011, Amsterdam, The Netherlands [Google Scholar]
- Shafiee, M. (2016). Modelling and analysis of availability for critical interdependent infrastructures. International Journal of Risk Assessment and Management, 19(4), 299–314. [CrossRef] [Google Scholar]
- Davies, A. (Ed.). (2012). Handbook of condition monitoring: techniques and methodology. Springer Science & Business Media. [Google Scholar]
- Sunder Selwyn T., Hemalatha S., (2020), “Experimental analysis of mechanical vibration in 225kW wind turbine gear box”,Materials Today: Proceedings,Vol.46,no.,pp.3292–3296.doi:10.1016/j.matpr.2020.11.461 [Google Scholar]
- H.D.M.de Azevedo, A.M., Araújo, A. M., & Bouchonneau, N. (2016). A review of wind turbine bearing condition monitoring: State of the art and challenges. Renewable and Sustainable Energy Reviews, 56, 368–379. [CrossRef] [Google Scholar]
- C.J. Crabtree, Y. Feng, & P.j. Tavner, “ Detecting incipient wind turbine gearbox failure: a signal analysis method for on-line condition monitoring”. In Proceedings of European Wind Energy Conference (EWEC 2010), Warsaw, Poland (Vol. 2023, p. 154156). [Google Scholar]
- C.J. Crabtree, “ Survey of Commercially Available Condition Monitoring Systems for Wind Turbines”, Durham University: Durham, UK, 2010. [Google Scholar]
- Chen, B. Survey of Commercially Available SCADA Data Analysis Tools for Wind Turbine Health Monitoring; School of Engineering and Computing Sciences, Durham University: Durham, UK, 2010. [Google Scholar]
- P. Tchakoua, R. Wamkeue, M. Ouhrouche, F. Slaoui-Hasnaoui, T. Tameghe, and G. Ekemb, “Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges,” Energies, vol. 7, no. 4, pp. 2595–2630, Apr. 2014, doi: 10.3390/en7042595. [CrossRef] [Google Scholar]
- Sunder Selwyn T., Hemalatha S., (2020), “Condition monitoring and vibration analysis of asynchronous generator of the wind turbine at high uncertain windy regions in India”, Materials Today: Proceedings, Vol. 46, no.,pp.3639–3643.doi:10.1016/j.matpr.2021.01.656. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.