Structural performance degradation identification of offshore wind turbines based on variational mode decomposition with a Grey Wolf Optimizer algorithm

Abstract

The performance degradation assessment of offshore wind turbine (OWT) structures plays a crucial role in ensuring the safe operation of the structures. This paper presents a method for assessing the performance degradation of OWT structures based on an optimized variational mode decomposition (VMD) algorithm. The method introduces the technique of multisensor data fusion and the Grey Wolf Optimizer (GWO) algorithm to optimize the setting of VMD parameters and then enables the assessment of the structural performance degradation of offshore wind turbines by extracting the structural performance degradation signature of the structure from noise-reduction data. To demonstrate the correctness and advantages of the method in this paper, a 4-degree-of-freedom (4-DOF) system under the action of white noise is used. Numerical calculations show that the developed method can accurately identify the 5%, 10%, 15% or 20% reduction in the structural stiffness and the coefficient of variance of the optimal structure is decreased to 3.6% of the initial design. To further investigate the performance of the proposed strategy, physical tests of the offshore wind monopile structure are conducted. As the loss of the overall structural performance is simulated by removing different numbers of bolts from the connected device, it is demonstrated that the proposed method can effectively assess the structural performance. Finally, field measurements are carried out on a monopile OWT located near Rudong County, Jiangsu Province, in the Yellow Sea of China. The impact of typhoons on the performance of the structure is effectively assessed by analyzing measurement data before and three days after the typhoon In-Fa through the wind farm, the potential application of the method in the structural safety assessment and analysis of OWTs is validated.