Cooperative yaw control of wind farm using a double-layer machine learning framework

Abstract

An appropriate cooperative yaw control strategy, in which the accurate power prediction and efficient optimization method play an essential role, can mitigate the significant power loss owing to the wake interference. The present study develops a novel double-layer machine learning framework, which combines an artificial neural network (ANN) yawed wake model and a Bayesian machine learning algorithm. The former undertakes the power prediction as the 1st layer, which is fed to the 2nd layer of the optimization system. Using computational fluid dynamics (CFD) simulation result as a reference, the performance of the proposed framework is evaluated compared with the framework based on a Gaussian-based analytical wake model by Qian and Ishihara. Furthermore, parametric studies have been conducted on the inflow conditions to explore its applicability. The results show that the proposed framework can realize more accurate power prediction with a mean error of 1.13% and 0.96% for different inflow velocities and turbulence intensities respectively, in contrast with the one based on the analytical model, 7.61% and 1.66%. Moreover, more notable power improvements can be achieved by the proposed framework, seeing a mean rise of 5.59% and 2.22% under four inflow velocities and turbulence intensities respectively, compared with 3.55% and 1.02% based on the analytical model. Specifically, its superiority becomes more obvious under relatively low inflow velocity and turbulence intensity.