Volume 02: Proceedings of 11th International Conference on Applied Energy, Part 1, Sweden, 2019

Identifying the Major Contributing Factors for Fowt Mooring Line Tension Using Artificial Neural Network Zi Lin, Xiaolei Liu

Abstract

Mooring lines are significant components for floating offshore wind turbines (FOWTs). Unlike offshore floating platforms, whose mooring systems are just for station-keeping, mooring lines for FOWTs are not only for station-keeping but may also have an effect on the global performance of FOWT and vice versa. Previous studies have reported mooring line damages under different operating conditions and questions have been raised about the reasons for those failures. To tackle this issue, this paper aims at studying the major contributing factors on FOWT mooring line tension. Potential influences on the mooring line fairlead tension have been grouped into forces and displacements. Using these forces and displacements as the input in an Artificial neural network (ANN) and the most loaded mooring line fairlead tension as the output, ANN was trained to investigate the significance of inputs to the mooring line tension. Under the operating condition, results from ANN showed that mooring line fairlead tension is heave motion dominated while blade root bending moment contributed first on mooring line tension in terms of forces.

Keywords Floating offshore wind turbines (FOWT); Artificial neural network (ANN); Mooring line tension; Mooring damage; Dynamic response

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