Offshore Wind Turbine (OWT) structural design can be optimized to reduce structural costs, thereby lowering wind energy costs. Increasing turbine size and further structural cost reduction require more advanced insights into the dynamic system. One practice of analysing these vibrating structures is called Operational Modal Analysis (OMA). OMA estimates parameters of a structural dynamic model of which damping is considered especially important due
to the direct relation with the magnitude of the response. Although OMA is an established field, damping estimation of operational Offshore Wind Turbines is often difficult due to violation of the stationary white noise excitation condition posed by classical OMA algorithms. Rotor rotation causes a harmonic loading on the non-rotating part of the structure, which is problematic for conventional
methods. Harmonics-mitigating OMA methods were developed in recent years to tackle this issue. Four state-of-the-art OMA algorithms are selected to identify the damping of an operational OWT. Each algorithm is applied to a dataset obtained from a simple simulation test case and a wind turbine simulation before considering field measurements. Subsequently, algorithm
robustness is examined by comparing estimates from multiple measured datasets with corresponding simulations and estimates from different algorithms for multiple wind speeds. It was found that the first mode corresponds well with the model for wind speeds under 15 m/s, whereas the second mode frequency has a large offset from the modelled value, revealing modelling flaws. Moreover, the estimates of the Modified Least-squares Complex Exponential (LSCE), PolyMAX, and Kalman Filter-based Stochastic Subspace Identification (KF-SSI)
algorithms correspond well for both measured and simulated data. They are well-suited for identifying the damping of operational OWTs. Cepstrum editing is not recommended for damping identification as the editing affects the damping value. Enhanced Power Spectral Density Transmissibility (PSDT) is not recommended when few sensor levels are available. Novel enhancements were developed in this thesis. First, the results of the KF-SSI algorithm were improved by concatenating datasets in the identification steps and reducing variance.
Also, numerical issues were addressed by implementing the Square Root Covariance Filter (SRCF) instead of the conventional Kalman filter. Other developments include an algorithm for automated interpretation of stabilization charts and a harmonic localization method based on the rotor velocity.

Marine propulsion systems with diesel engines face continuous variations in propeller shaft speed and torque during operation, due to waves and changing rudder and water inflow angles. Traditional propulsion control systems do not take these variations into account, and attempt to maintain a constant shaft speed. These changing torques, in combination with inefficient control efforts, can increase mechanical wear and tear on the diesel engine, and reduce efficiency. More advanced control algorithms relying on the axial flow velocity of water through the propeller have shown great promise. Unfortunately, this velocity, also known as the advance velocity, is very difficult or impossible to measure. Therefore, it is possible to use state observers to estimate the advance velocity using available measurements of shaft speed and torque. However, the current state of the art regarding advance velocity estimation is very limited.In this paper, a complete model of a ship propulsion system is created. Several different observer structures are applied to the propulsion model to estimate the advance velocity. These include a Luenberger observer, a Shaft Kalman Filter (SKF), a Power Balance Estimator (PBE), a Measured Shaft Acceleration (MSA) observer with and without measurements of shaft acceleration, an Immersion & Invariance (I&I) observer and a Random Walk Kalman Filter (RWKF). The performance of these advance velocity observers using different observer gains is compared using several load cases. These load cases are used to assess time domain accuracy with and without measurement noise, and robustness to errors in a priori information regarding the propulsion system. The findings indicate a clear trade off between noiseless accuracy, and robustness to noise. If an observer has high observer gains, it is more accurate when no noise is present, but more sensitive to measurement noise, and vice versa. Furthermore, higher gains also lead to more robustness to errors in a priori information. The errors that lead to the most significant estimation errors were errors in the open water propeller diagrams, with errors in the shaft inertia proving to be much less significant. That being said, if accurate shaft acceleration is available, the MSA observer is most effective. If this data is unavailable or inaccurate, a choice must be made. If measurement noise robustness is paramount, the RWKF or an I&I observer with low gains is most effective. If absolute accuracy is most important, and the measured variables are virtually free of noise, an I&I observer with high gains is the best choice. The RWKF provided a very good compromise between noise robustness and noiseless accuracy, but requires a large amount of a priori information. Suggestions for future research include testing the observers in both cavitation tunnels and full scale propulsion systems, and implementing multivariable propulsion control using the estimated advance velocity.

Classical feedback control for floating offshore wind turbines has limited capabilities to reduce wave disturbances. Feedforward control uses disturbance measurement, to construct an additional control law such that the disturbance is attenuated. The main objective of this work is to prove the concept of wave-FF control to attenuate wave disturbances. High-fidelity simulations demonstrate that FF control, based on wave measurements, is able to reject wave disturbances on the rotor speed effectively.