PhD defense Elie Kadoche: Deep reinforcement learning for wind farm flow control

Thursday March 20, 2025, at 14.00 (Paris time) at Télécom Paris

Télécom Paris, 19 place Marguerite Perey F-91120 Palaiseau [getting there], amphi 4 and in videoconferencing

Jury

Ana BUŠIĆ, Researcher, INRIA, PSL University, ENS (DIENS) (Reviewer).
Tristan CAZENAVE, Professor, PSL University, Dauphine (LAMSADE) (Reviewer).
David FILLIAT, Professor, IP Paris, ENSTA (U2IS) (Examiner).
Paul FLEMING, Researcher, NREL (National Wind Technology Center) (Examiner).
Pascal BIANCHI, Professor, IP Paris, Télécom Paris (S2A) (Supervisor).
Damien ERNST, Professor, IP Paris, Télécom Paris (S2A) (co-Supervisor).
Florence d’ALCHÉ-BUC, Professor, IP Paris, Télécom Paris (S2A) (Guest).
Florence CARTON, Researcher, TotalEnergies (Guest, co-Supervisor).
Philippe CIBLAT, Professor, IP Paris, Télécom Paris (S2A) (Guest, co-Supervisor).

Abstract

Within wind farms, wake interactions between turbines can significantly reduce overall energy production. Wind farm flow control encompasses methods designed to mitigate these effects through coordinated turbine control. Wake steering, for example, involves redirecting the wake of certain machines to optimize airflow and increase power output. This is achieved via yaw control, by intentionally misaligning certain turbines with the incoming wind. However, designing robust wake steering controllers is challenging: they must adapt to dynamic and uncertain wind conditions, respect turbine actuation constraints, and capture the complexity of physical interactions in large arrays.

Reinforcement learning offers a powerful framework for developing...

… more effective controllers. By leveraging principles from artificial intelligence, it enables wind farms to adapt and respond intelligently to diverse wind conditions. A reinforcement learning agent learns from past experience and continuously refines control strategies as the wind changes. This Ph.D. thesis further studies and develops deep reinforcement learning-based wake steering controllers for wind farm flow control.
More specifically: 1) it studies the impact of wind dynamics on yaw control and highlights the importance of optimizing strategies over a certain time horizon when variations in wind direction are significant; 2) it investigates how multi- agent reinforcement learning approaches can control large-scale wind farms and
capture complex wake interactions in time-varying wind conditions; 3) it leverages self-attention mechanisms within a single-agent setting to build more capable controllers, demonstrating significant improvements in sample efficiency, learning performance and generalization. Overall, this Ph.D. thesis contributes to narrowing the gap between algorithmic advances in deep reinforcement learning and their practical deployment for large-scale wind farm flow control.

And also: [Ideas] Intelligent wind turbines for optimised energy production