This inventor holds 1 USPTO granted patent and 1 EPO patent. Top assignees: Centre National De La Recherche Scientifique, IFP Energies Nouvelles, Institut National Polytechnique De Grenoble. Active years: 2023.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:




Years Active: 2023
Title: The Innovative Mind of Mazen Al-Amir
Introduction
Mazen Al-Amir is a notable inventor based in Saint Martin d'Heres, France. He has made significant contributions to the field of renewable energy, particularly in wind turbine technology. His innovative approach combines machine learning with traditional engineering methods to enhance the efficiency and longevity of wind turbines.
Latest Patents
Mazen Al-Amir holds a patent for a "Method and system for controlling a quantity of a wind turbine by choosing the controller via machine learning." This invention focuses on a method of controlling a wind turbine by automatically selecting a controller that minimizes fatigue. The method utilizes an offline constructed database of simulations and an online machine learning step to determine the optimal controller based on wind turbine fatigue criteria. This innovative approach allows for the automatic selection and switching of controllers according to the evolving wind conditions.
Career Highlights
Throughout his career, Mazen has worked with prestigious organizations such as the Centre National de la Recherche Scientifique and the Institut National Polytechnique de Grenoble. His experience in these institutions has allowed him to develop and refine his innovative ideas in the field of renewable energy.
Collaborations
Mazen has collaborated with talented individuals such as Domenico Di Domenico and Guillaume Sabiron. These partnerships have contributed to the advancement of his research and the successful implementation of his inventions.
Conclusion
Mazen Al-Amir is a pioneering inventor whose work in wind turbine technology exemplifies the integration of machine learning and engineering. His contributions are paving the way for more efficient renewable energy solutions.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile