PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Journal of Ocean Engineering and Technology10.26748/ksoe.2021.0182021354287-295Fault Classification of a Blade Pitch System in a Floating Wind Turbine Based on a Recurrent Neural NetworkSeongpil Cho, Jongseo Park, Minjoo Choihttp://joet.org/upload/pdf/KSOE-2021-018.pdf, http://joet.org/journal/view.php?doi=10.26748/KSOE.2021.018, http://joet.org/upload/pdf/KSOE-2021-018.pdf
Renewable Energy10.1016/j.renene.2020.12.11620211691-13Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networksSeongpil Cho, Minjoo Choi, Zhen Gao, Torgeir Moanhttps://api.elsevier.com/content/article/PII:S0960148120320565?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0960148120320565?httpAccept=text/plain
Renewable Energy10.1016/j.renene.2017.12.1022018120306-321Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbinesSeongpil Cho, Zhen Gao, Torgeir Moanhttps://api.elsevier.com/content/article/PII:S0960148117313149?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0960148117313149?httpAccept=text/plain
Volume 9: Ocean Renewable Energy10.1115/omae2020-189462020Impact of a Wind Turbine Blade Pitch Rate on a Floating Wind Turbine During an Emergency Shutdown OperationPauline Louazel, Daewoong Son, Bingbin Yuhttp://asmedigitalcollection.asme.org/OMAE/proceedings-pdf/doi/10.1115/OMAE2020-18946/6607518/v009t09a070-omae2020-18946.pdf, http://asmedigitalcollection.asme.org/OMAE/proceedings-pdf/doi/10.1115/OMAE2020-18946/6607518/v009t09a070-omae2020-18946.pdf
Journal of Physics: Conference Series10.1088/1742-6596/753/9/0920122016753092012Model-based fault detection of blade pitch system in floating wind turbinesS Cho, Z Gao, T Moanhttp://stacks.iop.org/1742-6596/753/i=9/a=092012/pdf, http://stacks.iop.org/1742-6596/753/i=9/a=092012?key=crossref.74b95313d6dde6aa52b0f280b806882f
Wind Energy10.1002/we.20902017Preview predictive control layer design based upon known wind turbine blade-pitch controllersW. H. Lio, B. Ll. Jones, J. A. Rossiterhttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fwe.2090, https://onlinelibrary.wiley.com/doi/full/10.1002/we.2090
Blade-Pitch Control for Wind Turbine Load Reductions10.1007/978-3-319-75532-8_2201811-49Background of Wind Turbine Blade-Pitch Load Reduction ControlWai Hou Liohttp://link.springer.com/content/pdf/10.1007/978-3-319-75532-8_2
Wind Energy10.1002/we.20892017Experimental validation of model-based blade pitch controller design for floating wind turbines: system identification approachN. Hara, S. Tsujimoto, Y. Nihei, K. Iijima, K. Konishihttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fwe.2089, https://onlinelibrary.wiley.com/doi/full/10.1002/we.2089
E3S Web of Conferences10.1051/e3sconf/202019403005202019403005Fault early warning of pitch system of wind turbine based on GA-BP neural network modelSihan Chen, Yongguang Ma, Liangyu Mahttps://www.e3s-conferences.org/10.1051/e3sconf/202019403005/pdf
Ocean Engineering10.1016/j.oceaneng.2021.1088972021228108897Individual/collective blade pitch control of floating wind turbine based on adaptive second order sliding modeCheng Zhang, Franck Plestanhttps://api.elsevier.com/content/article/PII:S0029801821003322?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0029801821003322?httpAccept=text/plain