US 11,725,626 B2
Method and system for controlling a quantity of a wind turbine by choosing the controller via machine learning
David Collet, Rueil-Malmaison (FR); Guillaume Sabiron, Rueil-Malmaison (FR); Domenico Di Domenico, Rueil-Malmaison (FR); and Mazen Al-Amir, Saint Martin d'Heres (FR)
Assigned to IFP ENERGIES NOUVELLES, Rueil-Malmaison (FR); CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE, Paris (FR); UNIVERSITE GRENOBLE ALPES, Saint Martin d'Heres (FR); and INSTITUT POLYTECHNIQUE DE GRENOBLE, Grenoble (FR)
Appl. No. 17/603,389
Filed by IFP Energies nouvelles, Rueil-Malmaison (FR); CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE, Paris (FR); UNIVERSITE GRENOBLE ALPES, Saint Martin d'Heres (FR); and INSTITUT POLYTECHNIQUE DE GRENOBLE, Grenoble (FR)
PCT Filed Mar. 27, 2020, PCT No. PCT/EP2020/058739
§ 371(c)(1), (2) Date Oct. 13, 2021,
PCT Pub. No. WO2020/212119, PCT Pub. Date Oct. 22, 2020.
Claims priority of application No. 1904071 (FR), filed on Apr. 16, 2019.
Prior Publication US 2022/0213868 A1, Jul. 7, 2022
Int. Cl. F03D 7/02 (2006.01); F03D 7/04 (2006.01)
CPC F03D 7/0224 (2013.01) [F03D 7/047 (2013.01); F03D 7/0292 (2013.01); F05B 2260/821 (2013.01); F05B 2260/84 (2013.01); F05B 2270/332 (2013.01); F05B 2270/8042 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of controlling a quantity of a wind turbine for which a list of plural controllers of the quantity of the wind turbine is available, comprising steps of:
a) constructing a database offline by simulating, for each controller of the list and for plural wind data, a cost function representative of fatigue of the wind turbine;
b) measuring wind data online;
c) determining online a controller from the list that minimizes fatigue of wind turbine for the measured wind data by machine learning from the database; and
d) controlling online the quantity of the wind turbine by use of the determined controller.