On wake-steering control: an experimental study on wind-farm optimization
Time: Fri 2025-10-31 10.00
Location: F3 (Flodis), Lindstedtsvägen 26 & 28
Video link: https://kth-se.zoom.us/j/63329835473
Language: English
Subject area: Engineering Mechanics Energy Technology
Doctoral student: Derek Micheletto , Teknisk mekanik, KTH Royal Institute of Technology
Opponent: Prof. Carlo Bottasso, TUM School of Engineering and Design
Supervisor: Jens H. M. Fransson, Skolan för teknikvetenskap (SCI), KTH Royal Institute of Technology; Antonio Segalini, KTH Royal Institute of Technology
QC 251010
Abstract
Wake interactions between upstream and downstream turbines in wind farms lead to substantial power losses and accelerate structural fatigue, thereby shortening the rotors lifespan. To mitigate these effects and increase the economic viability of wind power, extensive research has focused on developing flow-control strategies aimed at minimizing wake-induced losses. Among these, wake-steering control has proven particularly effective. This technique consists of misaligning, or yawing, specific turbines with respect to the incoming-wind direction, thus inducing lateral forces that deflect their wakes away from downstream machines. When the angles are optimised, the increased wind speed experienced by downwind turbines results in energy gains that exceed the losses incurred by the yawed rotors, amounting to an overall enhancement in farm efficiency. The feasibility of wake-steering control has been demonstrated in windtunnel experiments and numerical simulations. Yet, the reported gains in power output and the corresponding optimal yaw angles exhibit considerable variability. In addition, the majority of these investigations involved only a limited number of turbines, often arranged in a single streamwise-aligned column. This thesis presents a series of wind-tunnel experiments designed to quantify the efficacy of wake-steering control on wind farms composed of a large number of turbines arranged in multiple columns. An experimental setup was developed to enable automated control and performance monitoring of a wind farm subjected to a replicated atmospheric boundary layer inflow. Initially, the wake properties of an isolated turbine were characterised for various yaw angles and inflow conditions. A systematic evaluation of numerous yaw-angle configurations was then conducted in wind farms consisting of 9 and 20 turbines, yielding maximum power enhancements of 5.3% and 2.7%, respectively. The findings indicate that the efficacy of wake steering diminishes with increasing free-stream velocity. Additionally, qualitative differences were observed in the responses of individual columns, likely attributable to their position within the array and to inter-column interactions.