Synergy of random balance design method and intelligent optimization technique for model updating of the 4 MW offshore wind turbine benchmark

• Published in: Marine structures Vol. 93; p. 103533
• Main Authors: Xu, Kai, Meng, Ankang, Chang, Shuang, Liu, Dianzi, Liu, Fushun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2024
• Subjects: Field measured data; Improved particle swarm optimization; Model updating; Offshore wind turbine; Random balance design; Offshore wind turbine; Improved particle swarm optimization; Field measured data; Random balance design; Model updating
• ISSN: 0951-8339; 1873-4170
• DOI: 10.1016/j.marstruc.2023.103533

The technical challenges for model updating of real marine engineering structures include the extraction of modal parameters associated with incomplete measurement information and the correction of a large number of structural parameters. As it is difficult to verify the effectiveness of model updating methods for engineering applications, the representative models have to be adopted in finite element (FE) analysis or lab-scale tests, leading to the incomplete reflection of actual structural performances. Based on these factors, a practical model updating method is developed in this study to make full use of field measurement data from a 4 MW offshore wind turbine for the accurate estimation of structural parameters using the random balance designs-Fourier amplitude sensitivity test (RBD-FAST) strategy and an improved particle swarm optimization (IPSO). Leveraging the measured acceleration signals from offshore wind turbines under the conditions including operation, shutdown, collision and typhoon scenarios, the complex exponential decomposition method is applied to accurately extract the time-varying acceleration components (TVAC) for the construction of the frequency response function (FRF). Following that, RBD-FAST is implemented into IPSO with adaptive inertia weights and asynchronously varying learning factors to enable efficient selection of numerous updated physical parameters, thus improving time cost and computational accuracy. The correctness of the proposed method is verified by a numerical jacket platform model. Furthermore, the 4 MW offshore wind turbine benchmark is developed to assess the feasibility of the proposed model updating method using field measured data from different scenarios. Results show that the synergy of RBD-FAST and IPSO in the proposed method can accurately update parameters and minimize a maximum discrepancy of 0.8970% for the first-three orders of natural frequencies between the benchmark and the updated models in the collision scenario. Summarily, the present work provides a potential technique and practical engineering references for model updating of offshore wind turbines subject to harsh marine environments. •Synergy of random balance design and IPSO for model updating of wind turbines.•Development of the time-varying acceleration components for modal parameters.•The feasibility validation of the proposed method by field testing data.