Abstract
In this paper an intelligent controller is designed to obtain the maximum power of a large floating offshore wind turbine. The control of these turbines is more complex due to the strong loads they are subjected to and the uncertainty that comes from the environment, mainly wind and waves, and from its non-linear dynamics. In this case, the control goal is to maximize the output power of the wind turbine by controlling the rotor speed. An incremental PD-type fuzzy controller has been implemented; it generates the pitch angle reference. The performance of this control scheme on the NREL 5 MW floating offshore wind turbine has been compared with the internal control that is provided within the FAST software. Results are encouraging, showing that the intelligent control strategy is able to produce more energy.
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References
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)
Gomes, I.L.R., Melício, R., Mendes, V.M.F., Pousinho, H.M.I.: Wind power with energy storage arbitrage in day-ahead market by a stochastic MILP approach. Log. J. IGPL 28(4), 570–582 (2020)
Mikati, M., Santos, M., Armenta, C.: Electric grid dependence on the configuration of a small-scale wind and solar power hybrid system. Renew. Energy 57, 587–593 (2013)
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)
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)
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)
Li, Z., Adeli, H.: Control methodologies for vibration control of smart civil and mechanical structures. Expert Syst. 35(6), e12354 (2018)
Kim, C., Muljadi, E., Chung, C.C.: Coordinated control of wind turbine and energy storage system for reducing wind power fluctuation. Energies 11(1), 52 (2018)
Quiles, E., Garciia, E., Cervera, J., Vives, J.: Development of a test bench for wind turbine condition monitoring and fault diagnosis. IEEE Lat. Am. Trans. 17(06), 907–913 (2019)
Santos, M.: Un enfoque aplicado del control inteligente. Revista Iberoamericana de Automática e Informática Industrial RIAI 8(4), 283–296 (2011)
Galvani, P.A., Sun, F., Turkoglu, K.: Aerodynamic modeling of NREL 5-MW wind Turbine for nonlinear control system design: a case study based on real-time nonlinear receding horizon control. Aerospace 3(3), 27 (2016)
Acho, L.: A proportional plus a hysteretic term control design: a throttle experimental emulation to wind turbines pitch control. Energies 12(10), 1961 (2019)
Nasiri, M., Mobayen, S., Zhu, Q.M.: Super-twisting sliding mode control for gearless PMSG-based wind turbine. Complexity 2019 (2019). Article ID 6141607
Kim, D., Lee, D.: Hierarchical fault-tolerant control using model predictive control for wind turbine pitch actuator faults. Energies 12(16), 3097 (2019)
Civelek, Z.: Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm. Eng. Sci. Technol. Int. J. 23(1), 1–9 (2020)
Rocha, M.M., da Silva, J.P., De Sena, F.D.C.B.: Simulation of a fuzzy control applied to a variable speed wind system connected to the electrical network. IEEE Lat. Am. Trans. 16(2), 521–526 (2018)
Jonkman, J., Butterfield, S., Musial, W., Scott, G.: Definition of a 5-MW reference wind turbine for offshore system development (No. NREL/TP-500-38060). National Renewable Energy Lab. (NREL), Golden, CO (United States) (2009)
Civelek, Z., Lüy, M., Çam, E., Mamur, H.: A new fuzzy logic proportional controller approach applied to individual pitch angle for wind turbine load mitigation. Renew. Energy 111, 708–717 (2017)
Santos, M., De la Cruz, J.M., Dormido, S., De Madrid, A.P.: Between fuzzy-PID and PID-conventional controllers: a good choice. In: Proceedings of North American Fuzzy Information Processing, pp. 123–127. IEEE (1996)
NREL FAST. https://www.nrel.gov/wind/nwtc.html. Accessed 20 Aug 2020
Santos, M., Dormido, S., De La Cruz, J.M.: Fuzzy-PID controllers vs. fuzzy-PI controllers. In: Proceedings of 5th International Fuzzy Systems, vol. 3, pp. 1598–1604. IEEE (1996)
Acknowledgments
This work was partially supported by the Spanish Ministry of Science, Innovation and Universities MCI/AEI/FEDER Project number RTI2018-094902-B-C21.
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Serrano-Barreto, C., Santos, M. (2020). Intelligent Fuzzy Optimized Control for Energy Extraction in Large Wind Turbines. 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_26
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DOI: https://doi.org/10.1007/978-3-030-62365-4_26
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