Biomimetic individual pitch control for load alleviation

Abstract

Individual pitch control has shown great capability of alleviating the oscillating loads experienced by wind turbine blades due to wind shear, atmospheric turbulence, yaw misalignement or wake impingement. This work presents a bio-inspired structure for individual pitch control where neural oscillators produce basic rhythmic patterns of the pitch angles, while a deep neural network modulates them according to the environmental conditions. This mimics, respectively, the central patterns generators present in the spinal chord of animals and their cortex. The mimicry further applies to the neural network as it is trained with reinforcement learning, a method inspired by the trial and error way of animal learning. Large eddy simulations of the reference NREL 5MW wind turbine using this biomimetic controller show that the neural network learns how to reduce fatigue loads by producing smooth pitching commands.