The current energy crisis coupled with an unprecedented climate situation is bringing onshore and offshore wind energy to the forefront. For many years, IFPEN has focused its strategic axes to become a major actor of the energy transition and to allow wind energy to become competitive. In order to optimize the behavior of wind turbines (increase in efficiency, fatigue monitoring, ...) it is essential to define a realistic and proven wind profile to which the turbines are exposed. These wind profiles can be synthetic (stochastic simulators reproducing only a limited part of the real winds) or from experimental LIDAR remote sensing measurements (limited to the wind types present during the acquisition campaign). The objective of this thesis is to make it possible to generate synthetic, but plausible, wind fields by learning from a classified database of "proven" wind fields.
During this thesis, it is thus a question of carrying out a typology of the proven winds undergone by the wind turbine and thus to build a database of realistic winds. It will also define a generative architecture of type Time-GAN (Time - Generative Adverse Network) able to produce wind profiles. In a first step, we will validate this architecture by confronting it with wind profiles from current stochastic simulators. Once defined and validated in the controlled framework of simulation, this architecture can be challenged on real data from experimental campaigns. Indeed, the LIDAR data measurements to which the student will have access allow the use of adapted and mastered processing algorithms at IFPEN to generate an estimate of wind fields representative of what the wind turbine is exposed t
Keywords: Generative models, Time Series Data Generation · Generative Adversarial Network · Deep Neural Network · Data Augmentation · Synthetic Data Generation
- Academic supervisor Directeur de l’UFR de Science, DUMAS Laurent, UVSQ, ORCID
- Doctoral School Université Paris-Saclay – Mathématique Hadamard
- IFPEN supervisor PhD, LECOMTE Jean-François, Ingénieur R&I, R114, email@example.com, ORCID
- PhD location IFP Energies Nouvelles, Rueil-Malmaison, France
- Duration and start date 3 years, starting in fourth quarter 2023
- Employer IFP Energies Nouvelles, Rueil-Malmaison, France
- Academic requirements University Master degree in data science or equivalent
- Language requirements Fluency in French or English, willingness to learn French
- Other requirements Deep Learning and Generative Networks
To apply, please send your cover letter and CV to the IFPEN supervisor indicated here above.
IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site.
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions. For more information, please see our dedicated WEB pages.