Maxime Lejeune
PhD student
Ir. at UCL in 2018

Main project: WakeOpColl: Performance optimization of wind farms under realistic operating conditions using collaborative control
Funding: ERC
Supervisor(s): Philippe Chatelain

Fast-increasing demand for renewable energy has resulted in a growing interest for the development of new efficient wind farms. As a consequence, the study of wake effects has been gaining a lot of attention recently. Wake effects dictate the optimal operating point of wind farms. Indeed, placing wind turbines in close proximity to one another leads to convoluted wake-wake and wake-turbine interactions which have a detrimental effect on both the lifespan and the energy production of the turbines.
Developing flexible control strategies able to take into account the convoluted dynamic wake effects is thus currently one of the most prevailing challenges faced by the wind energy industry. Despite large amount of resources engaged on the topic, classical control and optimization theories applied to wind farms have up to this day failed to achieve high efficiency together with transparent adaptivity, robustness and flexibility.
Computational Fluid Dynamic (CFD) models based controllers have been introduced in an attempt to account for the wake effects. Even though they allowed to accurately capture the physic of wind turbine farm in given conditions, they still remained unsuitable for control under time varying atmospheric conditions due to their prohibitive computational cost
The aim of this project is thus to develop affordable wake simulation tools and then to apply them in the framework of machine learning and collaborative control in order to enhance the performances of the farms.

IMMC main research direction(s):
Computational science
Fluid mechanics

wake flows
wind turbine

Research group(s): TFL

Recent publications

See complete list of publications

Conference Papers

1. Lejeune, Maxime; Moens, Maud; Coquelet, Marion; Coudou, Nicolas; Chatelain, Philippe. Development of an online wind turbine wake model.

2. Lejeune, Maxime; Coquelet, Marion; Coudou, Nicolas; Moens, Maud; Chatelain, Philippe. Development and validation of a wake model fed by blade loads estimated wind conditions.

3. Lejeune, Maxime; Coquelet, Marion; Moens, Maud; Chatelain, Philippe. Characterisation and Online Update of a Vorticity-Based Wind Skeleton Wake Model.

4. Coquelet, Marion; Lejeune, Maxime; Moens, Maud; Bricteux, Laurent; Chatelain, Philippe. Local estimation of wind speed and turbulence using wind turbine blades as sensors.

5. Coquelet, Marion; Bricteux, Laurent; Lejeune, Maxime; Chatelain, Philippe. Biomimetic individual pitch control for wind turbines.

6. Lejeune, Maxime; Coquelet, Marion; Coudou, Nicolas; Moens, Maud; Chatelain, Philippe. Data assimilation for the prediction of wake trajectories within wind farms.