This study proposes a hybrid control strategy utilizing an adaptive super-twisting (ASTW) methodology and artificial neural network (NN) techniques to regulate the collective blade pitch (CBP) of floating wind turbines (FWTs) operating in high wind conditions (Region III). In the presence of imposed perturbations, compounded by the complex nature of wind turbine dynamics, this research utilizes radial basis function (RBF) neural networks to approximate the unknown elements of the model. This methodology guarantees the controller’s stability and robustness, independent of the system’s precise mathematical model. The adaptive law is derived through the Lyapunov method, ensuring the convergence of rotor speed to the desired trajectory by fine-tuning the learning rate. The incorporation of the ASTW method, accompanied by self-tuning gains, aims to accelerate convergence speed and mitigate chattering synergistically with the NN approximation. The proposed controller was tested using the OpenFAST simulator, confirming its dynamic and static performance as well as validating its efficacy across scenarios with and without the RBF neural network approximator.

The floating wind turbines equipped with permanent magnet synchronous generators are complex nonlinear physical systems with multiple degrees of freedom. However, the complexity of these systems, the presence of unmodeled dynamics, and external disturbances pose challenges in maximizing power extraction during low-wind periods. This paper focuses on addressing this challenge for a grid-connected spar-buoy type floating offshore wind turbine (FOWT) operating in Region II. We evaluate the performance of the adaptive sliding mode control and the twisting algorithm to maximize power extraction in the low-wind region and deliver generated electricity to the grid considering the dynamic models of these systems. The controllers’ performance is compared against the existing simplified adaptive super-twisting algorithm under identical operating conditions of the FOWT. The co-simulation of the National Renewable Energy Laboratory (NREL) FAST software and the MATLAB/Simulink demonstrate the robustness of the proposed controllers across different conditions regardless of disturbance inputs.

This paper introduces a new control strategy for large-power (25MW) floating wind turbines (FWTs). First, a robust nonlinear controller, an adaptive super-twisting (ASTW) approach, is employed. This method significantly reduces the modeling effort. The strategy is designed to address challenges such as the negative damping effect, which can occur beyond the rated wind speed. A traditional proportional-integral (PI) controller can mitigate this effect, but it is slow when applied to large FWTs due to the system’s small natural pitch frequency. Second, the proposed ASTW controller is compared with the ROSCO control, which is based on the traditional gain-scheduled PI controller. The simulations consider a complete aero-hydro-servo-elastic model of the system, including mooring lines, a floating platform, a tower, a generator, and a rotor. The results suggest that the ASTW controller can maintain good tracking of the rated rotational speed and power output while ensuring satisfactory platform stabilization and fatigue load reduction, thereby improving the overall efficiency of the FWT.

International audience

In this paper, a new approach for extracting the angular position estimation error (APEE) of an Interior Permanent Magnet Synchronous Machine (IPMSM) is proposed. APEE is obtained from the measurable currents in the stationary reference frame (α,β) without the use of high-frequency injection or Back-electromotive force (BEMF). Moreover, using the APEE and, from the mechanical system of the IPMSM, a High Order Sliding Mode Observer (HOSMO) is used to estimate the angular position, speed and disturbance of the IPMSM. An analysis of convergence of the proposed observer is established using a Lyapunov approach. Furthermore, experimental results and a comparative study are presented to illustrate the performance of the proposed approach.

In this paper, a novel adaptive super-twisting controller is used for a floating wind turbine system working in region III. The tuning of the controller with time varying gains does not need any information on the bounds of the system uncertainties. As it is known, the floating wind turbine is an extremely nonlinear system and a simple model to reproduce all its dynamics does not exist. For these reasons, the proposed new controller is well adapted to this kind of system. The control objectives are power regulation, platform pitch motion reduction and reduction of blades fatigue load. The proposed controller is tested on FAST simulator, and the obtained results show its high level capability compared to other control strategies.