One of the phases of the installation of Offshore Wind Turbines is the installation of the foundation for the turbine. Herein, a monopile is one of the most frequently used foundation-types. This monopile is a high steel cylinder, over 60 meters high, which supports the wind turbine to the seabed. Currently, monopiles are lifted with large boom cranes on jack-up ships. SeaState5 has developed a more efficient method for monopile installation by using the Grasshopper-T (GH-T), a new crane which skids the monopiles from the deck and places it onto the seabed. The system reduces the weight and footprint for jack-up ships.

This thesis consists of mechanical modeling the GH-T for the purpose of estimating the forces within the lifting system. A non-linear modeling method; Euler-Lagrange
(EL) method with constraints is used to capture the systems dynamic, non-smooth and non-linear behavior over the entire range of motions of the upending process. Secondly, for the purpose of simplification, yet capturing the mentioned behavior, the system is linearized around several areas of operation, analyzing local detectablity and observability. Thirdly, attempts are made to design linear observers and disturbance estimator to both estimate the states of the system and any disturbance applied on the monopile, like hydrodynamic forces. Finally, a non-linear observer is proposed to estimate the states along the entire upending process of the monopile. Experiments for model validation are performed on SeaState5’s 1:25 scale prototype for which a sensor setup has been designed.

Simulations show that linear observers based on the linearized model are able to estimate the states, and compute forces of interest, within a very small area of operations. The designed linear disturbance estimator proves to be ineffective in estimating disturbances and is only valid for the same small neighborhood around the equilibrium point. This motives the need for an observer capable of estimation over a broad range of motions. Therefore, a non-linear observer is designed. For the non-linear observer, the state recovery proves to be efficient and robust enough to cope with sensor noise and the high range of motions of the upending procedure. Experimental results show the model is able to capture the non-linear behavior of the real system and estimate forces of interest within acceptable margins, showing promises for an online state estimation using the non-linear observer.