Hybrid ISC-DSC neural control for maximum energy extraction in offshore wind turbines
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12141Keywords:
DSC, ISC, Neural networks, MPPT, Power coefficient, Offshore wind turbineAbstract
In this research, the control operation of a 1.5MW floating offshore wind turbine (FOWT) is studied for maximum power point tracking (MPPT) using a hybrid ISC-DSC neural control strategy. The proposed controller applies a neural network that is trained with indirect speed control (ISC) system data, and integrated as part of a direct speed control (DSC) scheme to close the control loop. A realistic offshore wind turbine model, simulated with OpenFAST software, is subjected to environmental conditions of wind and waves that alter the dynamics of the system, increasing its complexity. Even in the face of these difficulties, it is demonstrated that the proposed controller generates satisfactory results in comparison with the OpenFAST embedded control system used as a reference. Higher energy production is achieved without causing relevant impacts on the movement of the structure.
References
Ayala E., and Simani, S. (2019). Perturb and observe maximum power point tracking algorithm for permanent magnet synchronous generator wind turbine systems. In: Conte G (ed) Proceedings of 15th European workshop on advanced control and diagnosis – ACD. Lecture notes in control and information sciences. Alma Mater Studiorum, University of Bologna. Springer, Bologna, pp 1–11.
Buestán-Andrade, P. A., Santos, M., Sierra-García, J. E., & Pazmiño-Piedra, J. P. (2023, August). Comparison of LSTM, GRU and transformer neural network architecture for prediction of wind turbine variables. In International conference on soft computing models in industrial and environmental applications (pp. 334-343). Cham: Springer Nature Switzerland.
Chandrasekaran, K., Mohanty, M., Golla, M., Venkadesan, A., and Simon, S. P. (2020). Dynamic MPPT Controller Using Cascade Neural Network for a Wind Power Conversion System with Energy Management. IETE Journal of Research, 1–15.
El Aissaoui, H., El Ougli, A., and Tidhaf, B. (2021). Neural networks and fuzzy logic based maximum power point tracking control for wind energy conversion system. Advances in Science, Technology and Engineering Systems Journal, 6(2):586–592
Magdi M., and Mojeed, O. (2019). Adaptive and predictive control strategies for wind turbine systems: a survey. IEEE Journal Autom SINICA 6:364–378
Malobe, P., Djondine, P., Eloundou, P. and Ndongo, H. (2020). A Novel Hybrid MPPT for Wind Energy Conversion Systems Operating under Low Variations in Wind Speed. Energy and Power Engineering, 12(12), pp.716-728.
Muñoz, E., Ayala, E., Pozo, N., and Simani, S. (2020). Fuzzy PID Control System Analysis for a Wind Turbine Maximum Power Point Tracking Using FAST and Matlab Simulink. In Brazilian Technology Symposium (pp. 905-917). Springer, Cham.
Muñoz, E., Ayala, E., and Pozo, N. (2021). Estrategia de Control Fuzzy PI en una Turbina Eólica con Generador de Inducción Doblemente Alimentado para Maximizar la Extracción de Potencia en Presencia de Perturbaciones. Revista Técnica "energía", 18(1), 1-10.
Muñoz-Palomeque, E, Sierra-García, J.E., and Santos.M, (2023). Wind turbine maximum power point tracking control based on unsupervised neural networks. Journal of Computational Design and Engineering 10.1: 108-121.
Muñoz-Palomeque, E., Sierra-García, J. E., & Santos, M. (2024a). Técnicas de control inteligente para el seguimiento del punto de máxima potencia en turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial, 21(3), 193-204.
Muñoz-Palomeque, E., Sierra-García, J. E., & Santos, M. (2024b). Enhancing Offshore Wind Turbines Performance with Hybrid Control Strategies Using Neural Networks and Conventional Controllers. Journal of Computational Design and Engineering, qwae103.
Ospina Álvarez, A. F., and Santos, M. (2022). Mechanical stability analysis of a DFIG floating offshore wind turbine using an oriented-control model. IEEE Latin America Transactions, 100.
Pande, J., Nasikkar, P., Kotecha, K. and Varadarajan, V. (2021). A Review of Maximum Power Point Tracking Algorithms for Wind Energy Conversion Systems. Journal of Marine Science and Engineering, 9(11), p.1187.
Pathak, G., Singh, B., and Panigrahi, B. K. (2016). Back-Propagation Algorithm-Based Controller for Autonomous Wind–DG Microgrid. IEEE Transactions on Industry Applications, 52(5), 4408–4415.
Pozo, A., Ayala, E., Simani, S., and Muñoz, E. (2021). Indirect Speed Control Strategy for Maximum Power Point Tracking of the DFIG Wind Turbine System. Revista Técnica "energía", 17(2), 92-101.
Pustina, L., Lugni, C., Bernardini, G., Serafini, J., and Gennaretti, M. (2020). Control of power generated by a floating offshore wind turbine perturbed by sea waves. Renew. Sustain. Energy Rev., vol. 132, p. 109984.
Sahri, Y., Tamalouzt, S., and Belaid, S. L. (2018). Direct torque control of DFIG driven by wind turbine system connected to the grid. In 2018 International Conference on Wind Energy and Applications in Algeria (ICWEAA) (pp. 1-6). IEEE.
Sierra-García, J. E., & Santos, M. (2020). Exploring reward strategies for wind turbine pitch control by reinforcement learning. Applied Sciences, 10(21), 7462.
Sierra-Garcia, J. E., and Santos, M. (2021a). Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control. Neural Computing and Applications, 1-15
Sierra-García, J. E., and Santos, M. (2021b). Neural networks and reinforcement learning in wind turbine control. Revista Iberoamericana de Automática e Informática industrial, 18(4), 327-335
Yang, B., Zhang, X., Yu, T., Shu, H., and Fang, Z. (2017). Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine. Energy conversion and management, 133, 427-443.
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Copyright (c) 2025 Jesus Enrique Sierra Garcia, Eduardo Muñoz, Matilde Santos
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