Wake steering of multirotor wind turbines

TL;DR

This study explores wake steering in multirotor wind turbines using yaw control, demonstrating potential for reducing wake losses and improving downstream power production through LES simulations and model comparisons.

Contribution

It introduces the application of yaw-based wake steering to multirotor turbines and compares low-fidelity models with LES results for the first time.

Findings

Yaw control significantly alters wake velocity deficits.

Power gains are achievable for downstream turbines.

Low-fidelity models reasonably predict wake trends.

Abstract

In this paper, wake steering is applied to multirotor turbines to determine whether it has the potential to reduce wind plant wake losses. Through application of rotor yaw to multirotor turbines, a new degree of freedom is introduced to wind farm control such that wakes can be expanded, channelled, or redirected to improve inflow conditions for downstream turbines. Five different yaw configurations are investigated (including a baseline case) by employing large-eddy simulations (LES) to generate a detailed representation of the velocity field downwind of a multirotor wind turbine. Two lower-fidelity models from single-rotor yaw studies (curled-wake model and analytical Gaussian wake model) are extended to the multirotor case and their results are compared with the LES data. For each model, the wake is analysed primarily by examining wake cross sections at different downwind distances. Further quantitative analysis is carried out through characterisations of wake centroids and widths over a range of streamwise locations, and through a brief analysis of power production. Most significantly, it is shown that rotor yaw can have a considerable impact on both the distribution and magnitude of the wake velocity deficit, leading to power gains for downstream turbines. The lower-fidelity models show small deviation from the LES results for specific configurations; however, both are able to reasonably capture the wake trends over a large streamwise range.