Abstract: This work is aimed at optimizing the wind turbine rotor speed setpoint algorithm. Several intelligent adjustment strategies have been investigated in order to improve a reward function that takes into account the power captured from the wind and the turbine speed error. After different approaches including Reinforcement Learning, the best results were obtained using a Particle Swarm Optimization (PSO)-based wind turbine speed setpoint algorithm. A reward improvement of up to 10.67% has been achieved using PSO compared to a constant approach and 0.48% compared to a conventional approach. We conclude that the pitch angle is the most adequate input variable for the turbine speed setpoint algorithm compared to others such as rotor speed, or rotor angular acceleration.
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González-González, A.; Etxeberria-Agiriano, I.; Zulueta, E.; Oterino-Echavarri, F.; Lopez-Guede, J.M. Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms. Energies 2014, 7, 3793-3809.
González-González A, Etxeberria-Agiriano I, Zulueta E, Oterino-Echavarri F, Lopez-Guede JM. Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms. Energies. 2014; 7(6):3793-3809.
González-González, Asier; Etxeberria-Agiriano, Ismael; Zulueta, Ekaitz; Oterino-Echavarri, Fernando; Lopez-Guede, Jose M. 2014. "Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms." Energies 7, no. 6: 3793-3809.