Wind turbines are typically designed for an operational life of 20-25 years. The operation of the assets can be extended beyond their design life if structural components have sufficient reserves left. One approach is to monitor fatigue loads and compare these with design assumptions to determine the remaining useful lifetime of the assets. A major challenge is that sensors for measuring the stress history, such as strain gauges, only deliver local information. Monitoring of every hot spot is technically and financially not feasible due to cost and access restrictions. In addition, strain gauges only have a limited lifetime when compared to accelerometers. Several response estimation or extrapolation methods have been proposed in literature to tackle this problem. All of them are Kalman filter based methods, with the exception of the Modal Decomposition and Expansion Method. A new Kalman filter based method has been recently proposed in literature called Gaussian Process Latent Force Model. The aim of this work is to assess this new method with respect to existing ones both theoretically and numerically. Theoretically, the Kalman filter based methods always rely on white gaussian noise assumptions for the unknown loads, and the modal decomposition and expansion method disregard measurement imperfections. The new method improves upon these assumptions by providing a flexible stochastic definition for the unknown load, and by taking into account measurement imperfections. The numerical analysis is restricted to a comparison with respect to the modal decomposition and expansion method given the different theoretical backgrounds. The comparison is realised upon a simulation which illustrates and validates the methods, and upon a real-life onshore wind turbine equipped with accelerometers and strain gauges. Measured strains are compared with estimated strains. The accuracy of each method is quantified using the mean absolute error and by the correlation between measurement and estimation. The higher accuracy obtained shows that the new method is an improvement upon existing methods. This is further extended in the calculation of Damage Equivalent Loads. The result shows a relative error that, depending on operational conditions, ranges within 20-40[%] for the modal decomposition and expansion method and less than 10[%] for the new method. These results show that the novel Gaussian Process Latent Force Model method should be taken into account for response estimation when accuracy is relevant. Future works should aim on developing a mechanical model that better capture the real behaviour of the wind turbine, as the accuracy of the response estimation methods is mainly controlled by the validity of the underlying assumptions of the mechanical model. Furthermore, the strain estimations should be sought in the whole frequency range, this can be realised by including measurements that deliver this information: GPS sensors or inclinometers for example.

Optimization of structures in a domain with large uncertainties is rather difficult. This also applies for the offshore wind energy sector. For current offshore wind energy development locations with monopile-based support structures the fatigue limit state is the driving design criteria. These analyses are connected with long time domain evaluations to cover non-linearities. Model, statistical and data uncertainties lead to a combined fatigue damage prediction uncertainty. The former are either covered by a design fatigue factor or a material factor, which are stated in certification standards, e.g. DNVGL-ST-0126. The influence of mass changes regarding different lifetimes and the impact of this design fatigue factor has not been published yet.

Based on this, within this graduation project, the monopile support structure is optimized for different lifetimes in order to identify mass changes and influences of the design fatigue factor. Literature shows that automatized optimization using genetic algorithms in offshore wind energy is possible but limited, due to the algorithm methodology including a large number of design evaluations. This graduation project shows the applicability of Importance Sampling for load case reduction in a genetic algorithm optimization for offshore wind. Compared to previous approaches Importance Sampling assists to use a full certification procedure for fatigue limit state computations in a feasible amount of time with high fatigue life estimation accuracies. Subsequently, the fatigue limit state load case table is reduced by 93%. By optimizing the monopile with this reduced amount of load cases the algorithm is computationally feasible for the industry.

Rambøll simulation software for offshore wind turbine support structure design is used in combination with the genetic algorithm function in Matlab®. The combination of the software leads to the optimization of monopile based offshore wind support structures for different lifetimes. The algorithm runs with a reduced amount of load cases. Resulting critical fatigue damage values of converged designs are showing deviations from actual fatigue damage values using full fatigue limit state load case tables at maximum 6.6% and minimum 1.7%. This high accuracy leads to an optimization of monopile structures for desired lifetimes and consequently to the mass versus lifetime curve. A mass increase of approximately 22% is observed from 25 to 100 years lifetime. After reaching 75 years lifetime the curve shows a flattening behavior. Besides, parameter evolutions of optimized monopile designs are discussed in terms of different fatigue life. The design variables are embedment depth, cone angle, and corresponding wall thicknesses of monopile sections.

Summarized, this thesis proved the implementation of a full state of the art fatigue limit state computation in the genetic algorithm by Importance Sampling with reduced load cases and also visualized the impact of mass changes for different projected lifetimes. As a conclusive remark, the application of Importance Sampling for load case reduction in the design process opens new possibilities of optimization in the offshore wind energy sector.