Abstract
Offshore wind turbines, and particularly floating wind turbines (FOWT) are subjected to strong wind and wave loads that affect the structural stability and energy efficiency of these renewable energy devices. Although wind -and less often waves- forecasting models have been developed, a deep analysis of the relationship between both external disturbances is necessary to consider the combined effect on the fatigue of the offshore WT. This work presents a study of the most relevant features of wind and waves using distribution analysis and ML techniques on wind and waves real data from an offshore buoy. Linear regression and SVM have been applied to the modelling of the data. These models may be very useful for the design of these floating structures and to study the impact of these external loads on the fatigue. The results lead us to consider the necessity of generating short-term models in specific geographical locations.
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References
Mikati, M., Santos, M., Armenta, C.: Modelado y simulación de un sistema conjunto de energía solar y eólica para analizar su dependencia de la red eléctrica. Revista Iberoamericana de Automática e Informática Industrial 9(3), 267–281 (2012)
Ackermann, T.: Wind Power in Power Systems. Wiley, Hoboken (2005)
Mikati, M., Santos, M., Armenta, C.: Electric grid dependence on the configuration of a small-scale wind and solar power hybrid system. Renewable Energy 57, 587–593 (2013)
Tomás-Rodríguez, M., Santos, M.: Modelado y control de turbinas eólicas marinas flotantes. Revista Iberoamericana de Automática e Informática Industrial 16(4), 381–390 (2019)
Rubio, P.M., Quijano, J.F., López, P.Z., et al.: Intelligent control for improving the efficiency of a hybrid semi- submersible platform with wind turbine and wave energy converters. Revista Iberoamericana de Automática e Informática Industrial 16(4), 480–491 (2019)
Gomes, I.L.R., Melicio, R., Mendes, V.M.F.: Wind power with energy storage arbitrage in day-ahead market by a stochastic MILP approach. Logic J. IGPL 208, 570–582 (2019)
Li, Z., Adeli, H.: Control methodologies for vibration control of smart civil and mechanical structures. Exp. Syst. 35(6), e12354 (2018)
Stewart, G.M., Lackner, M.A.: The impact of passive tuned mass dampers and wind–wave misalignment on offshore wind turbine loads. Eng. Struct. 73, 54–61 (2014)
Trumars, J., Jonsson, J.O., Bergdahl, L.: The effect of wind and wave misalignment on the response of a wind turbine at Bockstigen. In: 25th International Conference on Offshore Mechanics and Arctic Engineering, pp. 635–641. American Society of Mechanical Engineers Digital Collection (2006)
Bachynski, E.E., Kvittem, M.I., Luan, C., Moan, T.: Wind-wave misalignment effects on floating wind turbines: motions and tower load effects. J. Offshore Mech. Arct. Eng. 136(4), 041902 (2014)
Bachynski, E.E., Moan, T.: Second order wave force effects on tension leg platform wind turbines in misaligned wind and waves. In: ASME 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers Digital Collection (2014)
Sun, C., Jahangiri, V.: Fatigue damage mitigation of offshore wind turbines under real wind and wave conditions. Eng. Struct. 178, 472–483 (2019)
Oh, S., Iwashita, T., Suzuki, H.: Numerical modelling and validation of a semisubmersible floating offshore wind turbine under wind and wave misalignment. J. Phys. Conf. Ser. 1104(1), 012010 (2018). IOP Publishing
NOAA: National oceanic and atmospheric administration. https://www.noaa.gov/. Accessed 19 Jun 2020
Stewart, G.M., Robertson, A., Jonkman, J., Lackner, M.A.: The creation of a comprehensive metocean data set for offshore wind turbine simulations. Wind Energy 19(6), 1151–1159 (2016)
Tukey, J.W.: Exploratory data analysis, vol. 2. Reading, Mass (1977)
Aguilar, R.M., Torres, J.M., Martin, C.A.: Automatic learning for the system identification. A case study in the prediction of power generation in a wind farm. Revista Iberoamericana de Automática e Informática Industrial 16(1), 114–127 (2019)
Guevara, C.B., Santos, M., Lopez, V.: Negative selection and Knuth Morris Pratt algorithm for anomaly detection. IEEE Latin Am. Trans. 14(3), 1473–1479 (2016)
Zhou, J., Shi, J., Li, G.: Fine tuning support vector machines for short-term wind speed forecasting. Energy Convers. Manag. 52(4), 1990–1998 (2011)
Dormido-Canto, S., et al.: TJ-II wave forms analysis with wavelets and support vector machines. Rev. Sci. Instrum. 75(10), 4254–4257 (2004)
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This work was partially supported by the Spanish Ministry of Science, Innovation and Universities MCI/AEI/FEDER Project RTI2018-094902-B-C21.
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Sacie, M., López, R., Santos, M. (2020). Exploratory Data Analysis of Wind and Waves for Floating Wind Turbines in Santa María, California. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_24
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