Patents by Inventor David COLLET

David COLLET has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • METHOD OF DETERMINING FREE-FLOW WIND SPEED FOR A WIND FARM
    Publication number: 20240328392
    Abstract: The present invention is a method of determining the free-flow wind speed (V?) for a wind farm, using measurements (MES), a wind farm model (MOD) and an ensemble Kalman filter (KEN).
    Type: Application
    Filed: March 25, 2024
    Publication date: October 3, 2024
    Inventor: David COLLET
  • Method and system for controlling a quantity of a wind turbine by choosing the controller via machine learning
    Patent number: 11725626
    Abstract: The present invention relates to a method of controlling a wind turbine by automatic online selection of a controller that minimizes the wind turbine fatigue. The method therefore relies on an (offline constructed) database (BDD) of simulations of a list (LIST) of controllers, and on an online machine learning step for determining the optimal controller in terms of wind turbine (EOL) fatigue. Thus, the method allows automatic selection of controllers online, based on a fatigue criterion, and switching between the controllers according to the measured evolution of wind condition.
    Type: Grant
    Filed: March 27, 2020
    Date of Patent: August 15, 2023
    Assignees: IFP ENERGIES NOUVELLES, CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE, UNIVERSITE GRENOBLE ALPES, INSTITUT POLYTECHNIQUE DE GRENOBLE
    Inventors: David Collet, Guillaume Sabiron, Domenico Di Domenico, Mazen Al-Amir
  • METHOD AND SYSTEM FOR CONTROLLING A QUANTITY OF A WIND TURBINE BY CHOOSING THE CONTROLLER VIA MACHINE LEARNING
    Publication number: 20220213868
    Abstract: The present invention relates to a method of controlling a wind turbine by automatic online selection of a controller that minimizes the wind turbine fatigue. The method therefore relies on an (offline constructed) database (BDD) of simulations of a list (LIST) of controllers, and on an online machine learning step for determining the optimal controller in terms of wind turbine (EOL) fatigue. Thus, the method allows automatic selection of controllers online, based on a fatigue criterion, and switching between the controllers according to the measured evolution of wind condition.
    Type: Application
    Filed: March 27, 2020
    Publication date: July 7, 2022
    Inventors: David COLLET, Guillaume SABIRON, Domenico DI DOMENICO, Mazen AL-AMIR