Development of clustering strategy to optimize the design of RNA and support structure in offshore wind farm under seismic conditions

Abstract

Offshore wind energy is one of the emerging renewable technologies toward a zero-carbon emission society. Historically, offshore wind farms have been developed in the North Sea, but nowadays wind farms are expanded to other regions such as North America and Asia, which are seismic prone.
In the design of Rotor-Nacelle-Assembly (RNA) and support structure for offshore wind farms, the number of design positions is reduced by using representative design positions (i.e. clustering). The number of designs is directly related to the efficiency of the design work, so it is worthwhile to minimize it by wisely selecting the representative position. However, such a clustering methodology has not been systematically developed in seismic design due to the limited number of projects and researches in seismic regions.
As for seismic analysis, there are several challenges to develop an efficient clustering strategy. First, higher (and multiple) vibration modes are excited in the system consisting of the wind turbine itself in interaction with the soil. Second, blade vibration (and blade-tower coupling effect) has a significant role in the output forces. Third, energy intake from the seismic ground motion is dependent on the position due to soil amplification. Considering the problem statement above, this research develops a clustering strategy for seismic analysis to optimize the design process on RNA and support structure for offshore wind farms. To achieve this goal, as a first step, a simplified calculation model is created to efficiently carry out the sensitivity studies. A combination of point masses with Euler-Bernoulli beam is used for RNA model while lumped mass at the tower top is conventionally used in past researches, which allows to properly represent the blade-tower coupling effect contrary to most of past researches. Considering modal participation mass, higher vibration modes are truncated, and principle vibration modes are detected, which improves the computational efficiency. Both the frequency domain method and the response spectrum method are developed and compared with proven software, BhawC, to verify their accuracy. Then, a sensitivity study is carried out by using the developed model. Through the sensitivity study, the system property is approximately quantified as a form of ”scaling factor”. Combining the scaling factor with acceleration response spectrum, the maximum bending moment for an arbitrary position (i.e. arbitrary water depth, soil condition and bedrock depth) can be quickly estimated. Subsequently, the positions in a wind farm are sorted into groups with similar load trends based on the estimated bending moment; this is the basis of the clustering strategy proposed in this thesis. Finally, the strategy is applied to virtual wind farms and its applicability is verified. Although it shows some errors in the estimation of the maximum bending moment itself, the accuracy is considered satisfactory to detect the most loaded position (i.e. design representative position) and categorize the positions which have similar load trend, which are one of the main requirements for a reliable clustering methodology. Also, the strategy is applied to more realistic soil condition, such as inhomogeneous soil and its applicability and limitation are investigated.