  We develop a torque-pitch control framework using deep reinforcement learning
for wind turbines to optimize the generation of wind turbine energy while
minimizing operational noise. We employ a double deep Q-learning, coupled to a
blade element momentum solver, to enable precise control over wind turbine
parameters. In addition to the blade element momentum, we use the wind turbine
acoustic model of Brooks Pope and Marcolini. Through training with simple
winds, the agent learns optimal control policies that allow efficient control
for complex turbulent winds. Our experiments demonstrate that the reinforcement
learning is able to find optima at the Pareto front, when maximizing energy
while minimizing noise. In addition, the adaptability of the reinforcement
learning agent to changing turbulent wind conditions, underscores its efficacy
for real-world applications. We validate the methodology using a SWT2.3-93 wind
turbine with a rated power of 2.3 MW. We compare the reinforcement learning
control to classic controls to show that they are comparable when not taking
into account noise emissions. When including a maximum limit of 45 dB to the
noise produced (100 meters downwind of the turbine), the extracted yearly
energy decreases by 22%. The methodology is flexible and allows for easy tuning
of the objectives and constraints through the reward definitions, resulting in
a flexible multi-objective optimization framework for wind turbine control.
Overall, our findings highlight the potential of RL-based control strategies to
improve wind turbine efficiency while mitigating noise pollution, thus
advancing sustainable energy generation technologies
