Abstract
The natural operation of wind turbines (WT) shows a nonlinear behavior which makes it difficult for the system to be controlled. Because of this, artificial intelligence techniques appear as promising control solutions. In this work, artificial neural networks (ANN) are used to complement the Direct Speed Control (DSC) of a wind turbine. Specifically, a neural network is used for the Maximum Power Point Tracking (MPPT) of a wind turbine model, controlling the generator speed and maintaining the active power into the correct levels to reach a power coefficient (\({C}_{p}\)) within its optimum values. The real characteristics of a 1.5 MW wind turbine are considered. OpenFast and Matlab/Simulink software tools are used to model and simulate the non-linear WT and the controller, respectively. The intelligent proposed solution is compared with the standard control embedded in OpenFast with satisfactory results.
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Acknowledgments
This work has been partially supported by the Spanish Ministry of Science and Innovation under the project MCI/AEI/FEDER number RTI2018–094902-B-C21.
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Muñoz Palomeque, E., Sierra-García, J.E., Santos, M. (2023). Complementing Direct Speed Control with Neural Networks for Wind Turbine MPPT. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_48
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DOI: https://doi.org/10.1007/978-3-031-18050-7_48
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