Recurrent neural network for pitch control of variable-speed wind turbine

• Published in: Science progress (1916) 2024-04, Vol.107 (2), p.368504241243160-368504241243160
• Main Authors: Asghar, Aamer Bilal, Ehsan, Raza, Naveed, Khazina, Al-Ammar, Essam A., Ejsmont, Krzysztof, Nejman, Mirosław
• Format: Article
• Language: English
• Subjects: Adaptive systems; Alternative energy sources; Artificial intelligence; Artificial neural networks; Cards; Computer & Information Sciences; Controllers; Cost effectiveness; Data acquisition; Feedforward control; Fuzzy control; Fuzzy logic; Machine learning; Matlab; Mean square errors; Multilayer perceptrons; Neural networks; Pitch (inclination); Recurrent neural networks; Renewable energy sources; Renewable resources; Root-mean-square errors; Soft computing; Speed control; Tip speed; Turbine blades; Turbines; Wind power; Wind speed; Wind turbines; Wind variations
• ISSN: 0036-8504; 2047-7163
• DOI: 10.1177/00368504241243160

Wind is one of the most widely used renewable energy sources due to its cost-effectiveness, power requirements, operation, and performance. There are many challenges in wind turbines, such as wind fluctuation, pitch control, and generator speed control. When the wind speed exceeds its rated value, the pitch angle controller limits the generator output power to its rated value. In this research work, several soft computing techniques have been implemented for pitch control of variable-speed wind turbine. The data is collected for the National Renewable Energy Laboratory offshore 5 MW baseline wind turbine. Wind speed, tip speed ratio, and power coefficient are taken as inputs, and pitch angle as output. Machine learning and artificial intelligence-based techniques such as recurrent neural networks (RNNs), adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron feed-forward neural network (MLPFFNN), and fuzzy logic controller (FLC) are implemented on MATLAB, and their results are evaluated in terms of mean square error (MSE) and root mean square error (RMSE). The controllers have been implemented in MATLAB/Simulink to schedule the wind turbine blade pitch angle and keep the output power stable at the rated value. The experimental results show that RNN provided the best results for 15 neurons in hidden layers and 1000 epochs with MSE of 3.28e-11 and RMSE of 5.54e-06, followed by MLPFFNN with MSE of 2.17e-10 and RMSE of 1.56e-05, ANFIS with MSE of 8.5e-05 and RMSE of 9.22e-03, and FLC with MSE of 6.25e-04 and RMSE of 0.025. The proposed scheme is more reliable and robust and can be easily implemented on a physical setup by using interfacing cards such as dSPACE, NI cards, and data acquisition cards.