Data-driven repetitive control: Wind tunnel experiments under turbulent conditions

TL;DR

This paper presents a data-driven control method called SPRC tested in wind tunnel experiments, effectively reducing turbine blade loads under turbulent conditions and adapting to changing environments.

Contribution

Introduction of SPRC, a novel data-driven repetitive control method, demonstrated on a scaled wind turbine in turbulent wind conditions with adaptive capabilities.

Findings

Significant load reduction achieved under turbulence

SPRC adapts to changing operating conditions

Effective in wind tunnel experiments with limited actuator duty

Abstract

A commonly applied method to reduce the cost of wind energy, is alleviating the periodic loads on turbine blades using Individual Pitch Control (IPC). In this paper, a data-driven IPC methodology called Subspace Predictive Repetitive Control (SPRC) is employed. The effectiveness of SPRC will be demonstrated on a scaled 2-bladed wind turbine. An open-jet wind tunnel with an innovative active grid is employed to generate reproducible turbulent wind conditions. A significant load reduction with limited actuator duty is achieved even under these high turbulent conditions. Furthermore, it will be demonstrated that SPRC is able to adapt to changing operating conditions.