Abstract
The importance and future prospects of offshore wind power generation invite great efforts and investments to make it an efficient technology. A crucial aspect is the development of efficient control strategies, which in many cases require models to identify time accurately the state of the turbine at a given. These models must be simple enough not to increase the computational complexity of the control algorithm while being able to capture the nonlinearity and coupling of the wind devices. In this work, we exploit the possibility of using neural networks to identify a wind turbine control-oriented model to predict its power output. Two nonlinear autoregressive with exogenous inputs models, with different input variables, have been proposed, based on feedforward neural networks. Results are satisfactory in terms of model accuracy of an offshore 5MW WT even ruling out relevant variables.
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References
Council, G.W.E.: Global Wind Report 2022. GWEC, Brussels (2022)
Hu, R., Conghuan, L., Ding, H., Zhang, P.: Implementation and evaluation of control strategies based on an open controller for a 10 MW floating wind turbine. Renew. Energy 179, 1751–1766 (2021)
Wright, A.D., Fingersh, L.J.: Advanced control design for wind turbines Part I: control design, implementation, and initial tests. NREL, Golden (2008)
Wakui, T., Nagamura, A., Yokoyama, R.: Stabilization of power output and platform motion of a floating offshore wind turbine-generator system using model predictive control based on previewed disturbances. Renew. Energy 173, 105–127 (2021)
Jonkman, J., Butterfield, S., Musial, W., Scott, G.: Definition of a 5-MW reference wind turbine for offshore system development. NREL (2009)
Branlard, E., Jonkman, J., Dana, S., Doubrawa, P.: A digital twin based on OpenFAST linearizations for real-time load and fatigue estimation of land-based turbines. In: Journal of Physics: Conference Series, vol. 1618 (2020)
Wang, L., Zhang, Z., Long, H., Xu, J., Liu, R.: Wind turbine gearbox failure identification with deep neural networks. IEEE Trans. Industr. Inf. 13(3), 1360–1368 (2017)
Sun, H., Qiu, C., Lu, L., Gao, X., Chen, J., Yang, H.: Wind turbine power modelling and optimization using artificial neural network with wind field experimental data. Appl. Energy 280, 115880 (2020)
Zhang, J., Yan, J., Infield, D., Liu, Y., Lien, F.-S.: Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model. Appl. Energy 241, 229–244 (2019)
Sierra-García, J.E., Santos, M.: Improving wind turbine pitch control by effective wind neuro-estimators. IEEE Access 9, 10413–10425 (2021)
Sierra-Garcia, J.E., Santos, M.: Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control. Neural Comput. Appl. 34(13), 1–15 (2021). https://doi.org/10.1007/s00521-021-06323-w
Blanco Fernández, C., Sierra García, J.E., Santos, M.: Control de un laboratorio de control de temperatura mediante redes neuronales recurrentes. XLIII Jornadas de Automática 193–200 (2022)
Sierra-García, J.E., Santos, M.: Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Rev. Iberoamericana de Automática e Informática Ind. 18(4), 327–335 (2021)
Alonso, A., Zabaljauregi, A., Larrea, M., Irigoyen, E., Sanchís, J.: Studying the use of ANN to estimate state-space variables for MIMO systems in a NMPC strategy. In: 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol. 531, pp. 464–473 (2022). https://doi.org/10.1007/978-3-031-18050-7_45
Jonkman, J., Musial, W.: Offshore Code Comparison Collaboration (OC3) for IEA Task 23 Offshore Wind Technology and Deployment. NREL, Golden (2010)
https://github.com/OpenFAST/openfast. National Renewable Energy Laboratory, https://github.com/OpenFAST/openfast. Accessed 1 Sept 2022
Abadi, M., et al.: Tensorflow: a system for large scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)
Habibi, M.R., Hamid, B., Dragicevic, T.: Detection of false data injection cyber-attacks in DC microgrids based on recurrent neural networks. IEEE J. Emerg. Sel. Top. Power Electron. 9, 5294–5310 (2010)
Sierra-García, J., Santos, M.: Switched learning adaptive neuro-control strategy. Neurocomputing 452, 450–464 (2021)
Acknowledgement
This work was partially supported by the Spanish Ministry of Science, Innovation and Universities under MCI/AEI/FEDER Project no. PID2021-123543OB-C21.
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Blanco, C., Sierra-García, J.E., Santos, M. (2023). Forecasting of Wind Turbine Synthetic Signals Based on Nonlinear Autoregressive Networks. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_25
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DOI: https://doi.org/10.1007/978-3-031-34107-6_25
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