Abstract
Floating Offshore Wind Turbines (FOWTs) are surrounded by an environment with random phenomena (wind and waves) that disturb the ideal operation of these devices. In addition, its non-linear dynamics make the control of power generation more complex. In order to face these disturbances, achieving the maximum energy production and reducing as much as possible the vibrations of the turbine, in this work a control action is designed and applied in the Maximum Power Point Tracking (MPPT) operation region of a 5MW FOWT. A hybrid control architecture composed of intelligent and conventional regulators is defined. The intelligent controller is an unsupervised radial basis function neural network (RBNN), which is responsible for adjusting the electromagnetic torque to achieve optimal speed and power output. The conventional controller that complements the NN is a PID that seeks to reduce the movements of the tower. This control approach is incorporated into the Direct Speed Control (DSC) framework which determines the reference speed to follow. Control parameters have been optimized using genetic algorithms. This hybrid methodology is validated against the OpenFAST software torque control strategy, providing greater efficiency in terms of better power generation and vibration reduction.
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Pfeifer, A., Krajačić, G., Ljubas, D., Duić, N.: Increasing the integration of solar photovoltaics in energy mix on the road to low emissions energy system – economic and environmental implications. Renew. Energy 143, 1310–1317 (2019). https://doi.org/10.1016/j.renene.2019.05.080
Swibki, T., Salem, I.B., Amraoui, L.E.: Modeling and control of direct-drive PMSG-based offshore wind turbine under rigorous wind conditions. In: 2020 6th IEEE International Energy Conference (ENERGYCon) (2020).https://doi.org/10.1109/energycon48941.2020.9236563
Huynh, P.T., Tungare, S., Banerjee, A.: Maximum power point tracking for wind turbine using integrated generator-rectifier systems. IEEE Trans. Power Electron. 36, 504–512 (2020). https://doi.org/10.1109/tpel.2020.3002254
Sierra-García, J.E., Santos, M.: Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial 18(4), 327–335 (2021)
Zhang, X., Zhang, Z., Jia, J., Zheng, L.: A Maximum Power Point Tracking Control Method Based on Rotor Speed PDF Shape for Wind Turbines. Appl. Sci. 12(18), 9108 (2022). https://doi.org/10.3390/app12189108
Abdullah, M.A., Yatim, A.H.M., Tan, C.W., Saidur, R.: A review of maximum power point tracking algorithms for wind energy systems. Renew. Sustain. Energy Rev. 16(5), 3220–3227 (2012). https://doi.org/10.1016/j.rser.2012.02.016
Thongam, J.S., Ouhrouche, M.: MPPT control methods in wind energy conversion systems. In: Carriveau, R, (ed.) Advanced Topics in Wind Power, pp. 339–360. InTech (2011)
Pan, L., Zhu, Z., Xiong, Y., Shao, J.: Integral sliding mode control for maximum power point tracking in DFIG based floating offshore wind turbine and power to gas. Processes 9(6), 1016 (2021). https://doi.org/10.3390/pr9061016
Chojaa, H., Derouich, A., Chehaidia, S.E., Zamzoum, O., Taoussi, M., Elouatouat, H.: Integral sliding mode control for DFIG based WECS with MPPT based on artificial neural network under a real wind profile. Energy Rep. 7, 4809–4824 (2021). https://doi.org/10.1016/j.egyr.2021.07.066
Xiong, L., Li, P., Wang, J.: High-order sliding mode control of DFIG under unbalanced grid voltage conditions. Int. J. Electr. Power Energy Syst. 117, 105608 (2020). https://doi.org/10.1016/j.ijepes.2019.105608
Pande, J., Nasikkar, P., Kotecha, K., Varadarajan, V.: A review of maximum power point tracking algorithms for wind energy conversion systems. J. Mar. Sci. Eng. 9(11), 1187 (2021)
Muñoz-Palomeque, E., Sierra-García, J.E., Santos, M.: Wind turbine maximum power point tracking control based on unsupervised neural networks. J. Comput. Design Eng. 10(1), 108–121 (2023). https://doi.org/10.1093/jcde/qwac132
Sitharthan, R., Karthikeyan, M., Sundar, D.S., Rajasekaran, S.: Adaptive hybrid intelligent MPPT controller to approximate effectual wind speed and optimal rotor speed of variable speed wind turbine. ISA Trans. 96, 479–489 (2019). https://doi.org/10.1016/j.isatra.2019.05.029
Kumar, D., Chatterjee, K.: A review of conventional and advanced MPPT algorithms for wind energy systems. Renew. Sustain. Energy Rev. 55, 957–970 (2016). https://doi.org/10.1016/j.rser.2015.11.013
Noureddine, S., Morsli, S., Tayeb, A.: Optimized fuzzy fractional PI-based MPPT controllers for a variable-speed wind turbine. Wind Eng. 46(6), 1721–1734 (2022)
Zhao, L., Xue, L., Li, Z., Wang, J., Yang, Z., Xue, Y.: Progress on offshore wind farm dynamic wake management for energy. J. Mar. Sci. Eng. 10(10), 1395 (2022)
Raouf, A., Tawfiq, K.B., Eldin, E.T., Youssef, H., El-Kholy, E.E.: Wind energy conversion systems based on a synchronous generator: comparative review of control methods and performance. Energies 16(5), 2147 (2023)
Karthik, R., Harsh, H., Pavan Kumar, Y.V., John Pradeep, D., Pradeep Reddy, C., Kannan, R.: Modelling of neural network-based MPPT controller for wind turbine energy system. In: Suhag, S., Mahanta, C., Mishra, S. (eds.) Control and Measurement Applications for Smart Grid. Lecture Notes in Electrical Engineering, vol. 822, pp. 429–439. Springer, Singapore (2022)
Zhou, B., Zhang, Z., Li, G., Yang, D., Santos, M.: Review of key technologies for offshore floating wind power generation. Energies 16(2), 710 (2023)
Álvarez, A.F.O., Santos, M.: Mechanical stability analysis of a DFIG floating offshore wind turbine using an oriented-control model. IEEE Lat. Am. Trans. 21(1), 91–97 (2023)
Muñoz, E., Ayala, E., Pozo, N., Simani, S.: Fuzzy PID control system analysis for a wind turbine maximum power point tracking using FAST and Matlab simulink. In: Iano, Y., Saotome, O., Kemper, G., Mendes de Seixas, A.C., Gomes de Oliveira, G. (eds.) BTSym 2020. SIST, vol. 233, pp. 905–917. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75680-2_100
Acknowledgments
This work has been partially supported by the Spanish Ministry of Science and Innovation under project MCI/AEI/FEDER number PID2021-123543OB-C21.
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Muñoz-Palomeque, E., Sierra-García, J.E., Santos, M. (2023). Hybrid Intelligent Control for Maximum Power Point Tracking of a Floating Wind Turbine. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_42
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