Floating offshore wind turbines allow wind energy to be harvested in deep waters. However, additional dynamics and structural loads may result when the floating platform is being excited by wind and waves. In this work, the conventional wind turbine controller is complemented with a novel linear feedforward controller based on wave measurements. The objective of the feedforward controller is to attenuate rotor speed variations caused by wave forcing. To design this controller, a linear model is developed that describes the system response to incident waves. The performance of the feedback-feedforward controller is assessed by a high-fidelity numerical tool using the DTU 10MW turbine and the INNWIND.EU TripleSpar platform as references. Simulations in the presence of irregular waves and turbulent wind show that the feedforward controller effectively compensates the wave-induced rotor oscillations. The novel controller is able to reduce the rotor speed variance by 26%. As a result, the remaining rotor speed variance is only 4% higher compared to operation in still water.

Individual Pitch Control (IPC) is a well-known and, in normal operating conditions, effective approach to alleviate blade loads in wind turbines. However, in the case of a Pitch Actuator Stuck (PAS) type of fault, conventional IPC is not beneficial since its action is disturbed by the failed pitch actuator. In this paper, a Subspace Predictive Repetitive Control (SPRC)-based IPC is proposed to implement a Fault Tolerant Control (FTC) strategy for Floating Offshore Wind Turbines (FOWTs) affected by PAS faults. In particular, an online subspace identification step is first carried out to obtain a linearized model of the FOWT system in faulty condition. The identified FOWT system is then used to develop a repetitive control law. Consequently, the adaptive repetitive control solution is implemented on the remaining healthy pitch actuators, in order to accommodate the PAS fault. Results show the developed SPRC approach allows to accommodate the PAS faults, achieving a considerable reduction of the blade loads in combination with lower pitch activities for the healthy actuators. This allows to continue power production and postpone maintenance operations, thus reducing the OM costs.

This paper presents a new hardware-in-the-loop methodology for wave-basin scale-model experiments about floating offshore wind turbines and its application as a tool for the validation of control strategies. In the hardware-in-the-loop experiments, the physical Froude-scaled wind turbine model used in conventional scale-model tests is replaced by a numerical model, measurements and a multi-fan actuator. As usual, properly-scaled waves are generated in the wave basin and the floating platform is simulated by means of a scale-model. The hardware-in-the-loop methodology was used to recreate the interaction between the collective pitch controller and the platform pitch mode that, often observed in numerical studies. In addition, the blade-root load measurement available in the numerical model of the rotor was used to implement an individual pitch control strategy. Different from in conventional experiments, the hardware-in-the-loop methodology allows to recreate a realistic three-dimensional wind field that was used to demonstrate the effectiveness of the individual pitch control. The improved emulation of the rotor loads and wind field make the hardware-in-the-loop experimental methodology an effective tool for the development and validation of control strategies for floating offshore wind turbines.

The design of control strategies for floating offshore wind turbines (FOWTs) is even more difficult than for onshore and bottom-fixed offshore ones and a recognized control strategy for FOWTs is currently lacking. In order to design effective control strategies, the additional dynamics of these systems should be taken into account in the models used to solve this task. This paper presents the analytical derivation of a novel model conceived for control design purposes. In detail, the model is based on a linear description of the highly non-linear phenomena that are relevant for an FOWT. The quasi-steady assumption is used to give a description of the aerodynamic loads and how these are influenced by the main control inputs. Hydrodynamic radiation and diffraction forces are introduced by means of linear-time-invariant parametric models. Simulation results shows that the proposed linear model is able to predict the structural response of the turbine system and the floating platform effectively in the case of control inputs, wind and wave disturbances. Compared to the nonlinear high-fidelity model, the proposed model shows similar results, however, without much complexity, which is promising in the desing of FOWT control strategies.

Floating Offshore Wind Turbines (FOWTs) are more prone to suffer from faults and failures than bottom-fixed counterparts due to the severe wind and wave loads typical of deep water sites. In particular, mooring line faults may lead to unacceptably high operation and maintenance costs due to the limited accessibility of FOWTs. Detecting the mooring line faults is therefore critical, but the application of Fault Detection (FD) techniques has not been investigated yet. In this paper, an FD scheme based on a wave-excited linear model is developed to detect in a reliable way critical mooring line faults occurring at the fairlead and anchor ends. To reach the goal, a linear model of the FOWT is obtained by approximating the wave radiation and incident wave forces. Based on this model, an observer is built to predict the rigid rotor and platform dynamics. The FD scheme is thus implemented by comparing the Mahalanobis Distance of the observer prediction error against a probabilistic detection threshold. Numerical simulations in some selected fault scenarios show that the wave-excited linear model can predict the FOWT dynamics with good accuracy. Based on this, the FD scheme capabilities are demonstrated, showing that it is able to effectively detect two critical mooring line faults.