| Summary: | The thesis presents the research on automated offshore wind turbine (OWT)installation. The aim of the project is to find innovative and cost-effective methods for installing and maintaining OWTs. Utilization of wind energy grows quickly in the past two decades. The trend of increasing turbine size reduces the costs of installation and grid connection per unit energy produced. However, the growing installation height challenges the OWT installation. The target of the present thesis is to develfi and floating OWTs (including individual components like blades or pre-assembled subsystems), by state-of-the-art automatic control theories, aiming for improved efficiency, increased operation safety, and reduced installation cost. In the present thesis, two installation strategies are studied, i.e., single blade installation to a bottom-fixed OWT with a monopile foundation using a jackup vessel and tower-nacelle-rotor preassembly installation to a spar foundation using a catamaran. Depending on the level of onshore preassembly, they are two opposite extremes among all installation strategies. The former strategy has a wider application with the lowest operational efficiency, while the latter has the fewest offshore lifts but requires more specific equipment. The major emphasis is on automated single blade installation. Since blade is the component with the most complex aerodynamics characteristics and its installation is the most time-consuming and expensive, the research can be extended to other components can be easily solved. Furthermore, part of the effort is put on improving the performance of the novel wind turbine installation concept using a catamaran proposed by SFI MOVE. A user-friendly numerical modeling framework for the offshore installation is developed for control design purposes. Various OWT installation models have been investigated. Several controllers and estimators have been devel- oped. Time-domain simulations and sensitivity studies have been conducted to verify the performance of the proposed algorithms. This work was supported by the Research Council of Norway (RCN) through the Centre for Research-based Innovation on Marine Operations (CRIMOVE, RCN-project 237929), and partly by the Centre of Excellence on Autonomous Marine Operations and Systems (NTNU AMOS, RCN-project 223254).
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