Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms

Asier González-González, Ismael Etxeberria-Agiriano, Ekaitz Zulueta, Fernando Oterino-Echavarri and Jose Manuel Lopez-Guede
Additional contact information
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

Energies, 2014, vol. 7, issue 6, 1-17

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.

Keywords: setpoint; wind turbines; PSO; Reinforcement Learning; pitch (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/7/6/3793/pdf (application/pdf)
https://www.mdpi.com/1996-1073/7/6/3793/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:7:y:2014:i:6:p:3793-3809:d:37220

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().