Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms
Asier González-González,
Ismael Etxeberria-Agiriano,
Ekaitz Zulueta,
Fernando Oterino-Echavarri,
Jose Manuel Lopez-Guede
Affiliations
Asier González-González
Tecnalia Research & Innovation, Industry and Transport Division, Parque Tecnológico de Álava, c/ Albert Einstein 28, Miñano 01510, Spain
Ismael Etxeberria-Agiriano
Department of Computer Languages and Systems, University College of Engineering, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain
Ekaitz Zulueta
Department of Systems Engineering & Automatic Control, University College of Engineering, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain
Fernando Oterino-Echavarri
Department of Electronic Technology, University College of Engineering, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain
Jose Manuel Lopez-Guede
Department of Systems Engineering & Automatic Control, University College of Engineering, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz 01006, Spain
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.
