Members

IMMC

Maxime Lejeune
PhD student
Ir. at UCLouvain in 2018
Contact

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
Energy
Fluid mechanics

Keywords:
aerodynamics
wake flows
wind turbine

Research group(s): TFL

Recent publications

See complete list of publications

Journal Articles

1. Lejeune, Maxime; Moens, Maud; Chatelain, Philippe. A meandering-capturing wake model coupled to rotor-based flow-sensing for operational wind farm flow prediction. In: Frontiers in Energy Research, (2022). doi:10.3389/fenrg.2022.884068 (Accepté/Sous presse). http://hdl.handle.net/2078.1/262173

Conference Papers

1. Lejeune, Maxime; Moens, Maud; Chatelain, Philippe. Extension and validation of an operational dynamic wake model to yawed configurations. In: The Science of Making Torque from Wind (TORQUE 2022) 01/06/2022 - 03/06/2022 Delft, Netherlands. Vol. 2265, no.2, p. 022018 (2022). IOP Publishing Ltd: Bristol BS1 6HG, United Kingdom, 2022 xxx. doi:10.1088/1742-6596/2265/2/022018. http://hdl.handle.net/2078.1/262170

2. Coquelet, Marion; Lejeune, Maxime; Moens, Maud; Riehl, James; Bricteux, Laurent; Chatelain, Philippe. Tackling control and modeling challenges in wind energy with data-driven tools. 2022 xxx. http://hdl.handle.net/2078.1/262231

3. Lejeune, Maxime; Moens, Maud; Coquelet, Marion; Coudou, Nicolas; Chatelain, Philippe. Data assimilation for the prediction of wake trajectories within wind farms. In: Journal of Physics: Conference Series. Vol. 1618, no.6, p. 062055 (2020). IOP Publishing Ltd, 2020 xxx. doi:10.1088/1742-6596/1618/6/062055. http://hdl.handle.net/2078.1/238249

4. Coquelet, Marion; Bricteux, Laurent; Lejeune, Maxime; Chatelain, Philippe. Biomimetic individual pitch control for load alleviation. In: Journal of Physics: Conference Series. Vol. 1618, no. 1, p. 022052 (2020). IOP Publishing, 2020 xxx. doi:10.1088/1742-6596/1618/2/022052. http://hdl.handle.net/2078.1/239854

5. Lejeune, Maxime; Moens, Maud; Coquelet, Marion; Coudou, Nicolas; Chatelain, Philippe. Development of an online wind turbine wake model. 2020 xxx. http://hdl.handle.net/2078.1/228689

6. Coquelet, Marion; Bricteux, Laurent; Lejeune, Maxime; Chatelain, Philippe. Biomimetic individual pitch control for load alleviation. 2020 xxx. http://hdl.handle.net/2078.1/244422

7. Lejeune, Maxime; Coquelet, Marion; Moens, Maud; Chatelain, Philippe. Characterisation and Online Update of a Vorticity-Based Wind Skeleton Wake Model. 2019 xxx. http://hdl.handle.net/2078.1/225804

8. Lejeune, Maxime; Coquelet, Marion; Coudou, Nicolas; Moens, Maud; Chatelain, Philippe. Data assimilation for the prediction of wake trajectories within wind farms. 2019 xxx. http://hdl.handle.net/2078.1/225821

9. Coquelet, Marion; Lejeune, Maxime; Moens, Maud; Bricteux, Laurent; Chatelain, Philippe. Local estimation of wind speed and turbulence using wind turbine blades as sensors. 2019 xxx. http://hdl.handle.net/2078.1/225970

10. Coquelet, Marion; Bricteux, Laurent; Lejeune, Maxime; Chatelain, Philippe. Biomimetic individual pitch control for wind turbines. 2019 xxx. http://hdl.handle.net/2078.1/225986