A Reference Open-Source Controller for Fixed and Floating Offshore Wind Turbines

Abstract

Abstract. This paper describes the development of a new reference controller framework for fixed and floating offshore wind turbines that greatly facilitates controller tuning and represents standard industry practices. The reference wind turbine controllers that are most commonly cited in the literature have been developed to work with specific reference wind turbines. Although these controllers have provided standard control functionalities, they are often not easy to modify for use on other turbines, so it has been challenging for researchers to run representative, fully dynamic simulations of other wind turbine designs. The Reference Open-Source Controller (ROSCO) has been developed to provide a modular reference wind turbine controller that represents industry standards and performs comparably to or better than existing reference controllers. The formulation of the ROSCO controller logic and tuning processes is presented in this paper. Control capabilities such as tip-speed ratio tracking generator torque control, minimum pitch saturation, wind speed estimation, and a smoothing algorithm at near-rated operation are included to provide a controller that is comparable to industry standards. A floating offshore wind turbine feedback module is also included to facilitate growing research in the floating offshore arena. All the standard controller implementations and control modules are automatically tuned such that a non-controls engineer or automated optimization routine can easily improve the controller performance. This article provides the framework and theoretical basis for the ROSCO controller modules and generic tuning processes. Simulations of the National Renewable Energy Laboratory (NREL) 5-MW reference wind turbine and International Energy Agency 15-MW reference turbine on the University of Maine semisubmersible platform are analyzed to demonstrate the controller's performance in both fixed and floating configurations, respectively. The simulation results demonstrate ROSCO's peak shaving routine to reduce maximum rotor thrusts by nearly 14 % compared to the NREL 5-MW reference wind turbine controller on the land-based turbine and to reduce maximum platform pitch angles by slightly more than 35 % when using the platform feedback routine instead of a more traditional low-bandwidth controller.

Land-based wind turbines with flexible rail-transportable blades – Part 1: Conceptual design and aeroservoelastic performance

“…The OpenFAST models are controlled via the Reference Open-Source Controller (ROSCO) (Abbas et al, 2021;NREL, 2020). ROSCO implements a pitch control logic with standard rotor speed tracking in above-rated wind speeds and a control logic with optimal tip-speed ratio tracking in belowrated wind speeds, where pitch is held constant.…”

Control of a Floating Wind Turbine on a Novel Actuated Platform

“…The dynamics of immediate interest for FOWT controller design are those of the generator and the platform pitch. The fourth-order wind turbine model construction followed here is described in detail in [1], with the most relevant aspects summarized here. A first-order model of a wind turbine describes the dynamics of the generator speed, ω g , with respect to the aerodynamic torque, τ a , and the generator resistance torque, τ g :…”

“…The USFLOWT controller is adapted from the reference open-source controller (ROSCO) and is tuned using an adaptation of the procedure implemented in the ROSCO toolbox [1]. The above-rated blade pitch controller uses proportional-integral (PI) feedback to regulate generator speed and uses a model-based automated tuning procedure to try to meet a desired closed-loop transient response.…”

“…Because these gain values are in general unique to each linear model, they are used to construct a piecewise gain schedule indexed for each operating point of interest. The ROSCO implementation uses interpolated indexing of β to determine the current operating point, which approximates the current operating point without estimating the wind speed [1]. Though this automated tuning process neglects many higher-order and nonlinear effects, it is a suitable method for designing the bandwidth of a controller without iterating on results from costly nonlinear simulations.…”

“…For the baseline controller, the generator torque is fixed at its rated value in above-rated winds. When turbulence takes the wind turbine into below-rated wind speeds, the blade pitch saturates and the generator torque is controlled using a kω 2 law [1,13].…”