LS
L. Starink
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As we strive to meet more of our global energy demand with renewables, floating offshore wind has gained popularity: floating turbines can access the stronger winds far out at sea and so generate more power per turbine. A key limiter of wind farm power production is wake interaction, causing estimated yield losses between five and twenty percent.
Semi-submersible floating turbines have a unique ability to reposition. They can use the yaw-induced turbine repositioning (YITuR) technique to reposition, letting downstream turbines escape their upstream neighbour's wake. Recently, the optimality of sinusoidal dynamic repositioning trajectories has been shown analytically, but not in a learning-based environment. This thesis is the first to prove the concept of dynamic floating wind turbine repositioning for wake escape via reinforcement learning (RL) in simulation.
Dynamic turbine repositioning via a Proximal Policy Optimisation (PPO) neural network control policy is a promising alternative to traditional look-up table wind farm control. Here, the problem is formulated as a partially observable Markov decision process (POMDP), using an array of two turbines. A model-free RL controller is trained using PPO, inside the Free Vortex Wake Model (FVWM), with an extension to enable repositioning motions. The optimal RL policies discovered are periodic, with frequencies between Strouhal numbers of 0.048 and 0.065 – near the platform natural-frequency Strouhal number of 0.058 – achieving array powers of 38-43% beyond the fully-waked baseline and 9-13% beyond the static yaw-only optimum.
A proof-of-concept laboratory setup for testing dynamic repositioning control was designed, built, and commissioned as part of this thesis. It consists of two scaled versions of the NREL 5MW reference turbine, with porous-disc rotors. Porous discs retain the necessary wake characteristics under yawing, but they exacerbate the thrust drop beyond what real three-bladed rotors would do. With adaptations per the roadmap provided, laboratory testing can be a strong candidate for speeding up training for sample-hungry model-free RL controllers for dynamic repositioning. ...
Semi-submersible floating turbines have a unique ability to reposition. They can use the yaw-induced turbine repositioning (YITuR) technique to reposition, letting downstream turbines escape their upstream neighbour's wake. Recently, the optimality of sinusoidal dynamic repositioning trajectories has been shown analytically, but not in a learning-based environment. This thesis is the first to prove the concept of dynamic floating wind turbine repositioning for wake escape via reinforcement learning (RL) in simulation.
Dynamic turbine repositioning via a Proximal Policy Optimisation (PPO) neural network control policy is a promising alternative to traditional look-up table wind farm control. Here, the problem is formulated as a partially observable Markov decision process (POMDP), using an array of two turbines. A model-free RL controller is trained using PPO, inside the Free Vortex Wake Model (FVWM), with an extension to enable repositioning motions. The optimal RL policies discovered are periodic, with frequencies between Strouhal numbers of 0.048 and 0.065 – near the platform natural-frequency Strouhal number of 0.058 – achieving array powers of 38-43% beyond the fully-waked baseline and 9-13% beyond the static yaw-only optimum.
A proof-of-concept laboratory setup for testing dynamic repositioning control was designed, built, and commissioned as part of this thesis. It consists of two scaled versions of the NREL 5MW reference turbine, with porous-disc rotors. Porous discs retain the necessary wake characteristics under yawing, but they exacerbate the thrust drop beyond what real three-bladed rotors would do. With adaptations per the roadmap provided, laboratory testing can be a strong candidate for speeding up training for sample-hungry model-free RL controllers for dynamic repositioning. ...
As we strive to meet more of our global energy demand with renewables, floating offshore wind has gained popularity: floating turbines can access the stronger winds far out at sea and so generate more power per turbine. A key limiter of wind farm power production is wake interaction, causing estimated yield losses between five and twenty percent.
Semi-submersible floating turbines have a unique ability to reposition. They can use the yaw-induced turbine repositioning (YITuR) technique to reposition, letting downstream turbines escape their upstream neighbour's wake. Recently, the optimality of sinusoidal dynamic repositioning trajectories has been shown analytically, but not in a learning-based environment. This thesis is the first to prove the concept of dynamic floating wind turbine repositioning for wake escape via reinforcement learning (RL) in simulation.
Dynamic turbine repositioning via a Proximal Policy Optimisation (PPO) neural network control policy is a promising alternative to traditional look-up table wind farm control. Here, the problem is formulated as a partially observable Markov decision process (POMDP), using an array of two turbines. A model-free RL controller is trained using PPO, inside the Free Vortex Wake Model (FVWM), with an extension to enable repositioning motions. The optimal RL policies discovered are periodic, with frequencies between Strouhal numbers of 0.048 and 0.065 – near the platform natural-frequency Strouhal number of 0.058 – achieving array powers of 38-43% beyond the fully-waked baseline and 9-13% beyond the static yaw-only optimum.
A proof-of-concept laboratory setup for testing dynamic repositioning control was designed, built, and commissioned as part of this thesis. It consists of two scaled versions of the NREL 5MW reference turbine, with porous-disc rotors. Porous discs retain the necessary wake characteristics under yawing, but they exacerbate the thrust drop beyond what real three-bladed rotors would do. With adaptations per the roadmap provided, laboratory testing can be a strong candidate for speeding up training for sample-hungry model-free RL controllers for dynamic repositioning.
Semi-submersible floating turbines have a unique ability to reposition. They can use the yaw-induced turbine repositioning (YITuR) technique to reposition, letting downstream turbines escape their upstream neighbour's wake. Recently, the optimality of sinusoidal dynamic repositioning trajectories has been shown analytically, but not in a learning-based environment. This thesis is the first to prove the concept of dynamic floating wind turbine repositioning for wake escape via reinforcement learning (RL) in simulation.
Dynamic turbine repositioning via a Proximal Policy Optimisation (PPO) neural network control policy is a promising alternative to traditional look-up table wind farm control. Here, the problem is formulated as a partially observable Markov decision process (POMDP), using an array of two turbines. A model-free RL controller is trained using PPO, inside the Free Vortex Wake Model (FVWM), with an extension to enable repositioning motions. The optimal RL policies discovered are periodic, with frequencies between Strouhal numbers of 0.048 and 0.065 – near the platform natural-frequency Strouhal number of 0.058 – achieving array powers of 38-43% beyond the fully-waked baseline and 9-13% beyond the static yaw-only optimum.
A proof-of-concept laboratory setup for testing dynamic repositioning control was designed, built, and commissioned as part of this thesis. It consists of two scaled versions of the NREL 5MW reference turbine, with porous-disc rotors. Porous discs retain the necessary wake characteristics under yawing, but they exacerbate the thrust drop beyond what real three-bladed rotors would do. With adaptations per the roadmap provided, laboratory testing can be a strong candidate for speeding up training for sample-hungry model-free RL controllers for dynamic repositioning.