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arXiv AI By Mart\'in de Frutos, Baptiste Corban, Claire Bizon Monroc, Ana Bu\v{s}i\'c, Donatien Dubuc, Jiamin Zhud, Esteban Ferrer

Noise Steering: Exploiting Wake Steering and Acoustic Directivity for Noise-Constrained Wind Farm Control via Reinforcement Learning

Read the original on arXiv AI →

Noise Steering is a reinforcement learning approach that jointly adjusts turbine yaw angles and power setpoints to maximize wind farm power while keeping noise within a 40 dB limit in a protected area. By learning to redirect both wakes and acoustic directivity, the method achieves only a 2–7.7 % power deficit compared to a 17 % loss with standard noise‑reduced operation. The policy remains effective when tested in higher‑fidelity simulations, showing promise for better power‑noise trade‑offs in wind farm control.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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