Lidar-assisted nonlinear output regulation of wind turbines for fatigue load reduction

Abstract

Optimizing wind turbine performance involves maximizing or regulating power generation while minimizing fatigue load on the tower structure, blades, and rotor. In this article, we explore the application of a novel turbine control methodology referred to as nonlinear output regulation (NOR) for improving turbine control performance. NOR constructs a torque balance equation under which the closed loops follow desired stable dynamics, and solves it for the generator torque in partial load operation and for the blade pitch angles in full load operation, in a unified manner across both operating regions. The regulation relies on an estimate of rotor-effective wind speed (REWS). We consider estimation based on the turbine's SCADA, in particular the immersion and invariance (I and I) estimator, as well as lidar. Furthermore, we propose to use an average of the I and I and lidar estimates, where the lidar buffer time is chosen to compensate I and I's delay, to obtain a real-time low-variation estimate. The performance of the NOR controller with the averaged I and I and lidar estimate is compared against a state-of-the-art baseline reference controller known as ROSCO in both its standard feedback-only configuration as well as an existing lidar-assisted control (LAC) version of ROSCO that uses a pitch feedforward. NOR, with the averaged I and I and lidar estimate, matches this lidar-assisted ROSCO rotor speed tracking improvements over feedback-only ROSCO, but also significantly reduces fatigue loads and actuator usage. In particular, the blade flapwise damage equivalent loads (DELs) reduction corresponds to a doubled lifespan, and pitch rate is reduced by more than a third. The reductions are achieved without sacrificing power generation.