Achieving Power-Noise Balance in Wind Farms by Fine-Tuning the Layout with Reinforcement Learning

Wind farms situated in proximity to residential areas present environmental challenges, primarily due to noise emissions. Rectangular and parallelogram layouts are commonly employed in current wind farm designs owing to their simplicity and visual appeal. However, such configurations often experience significant power loss under certain wind directions because of intense wake interactions. This paper proposes a layout fine-tuning strategy for low-noise wind farm design. Within a reinforcement learning framework integrated with an engineering wake model and a noise propagation model, the positions of two turbines (controlled by two variables) are optimized. The noise propagation model was validated for idealized long-range sound propagation over flat terrain with acoustically soft surfaces. A case study was conducted on a 12-turbine wind farm located on a flat plain in China, with a noise threshold of 45 dB(A) used to assess the noise impact area. Optimization results demonstrate that the proposed method achieves a balance between power output and noise reduction compared to the original regular layout: Annual Energy Production (AEP) increased slightly by 0.16%, while the noise impact area was reduced by 6.0%. Although these improvements appear modest, the potential of the proposed methodology warrants further investigation.