Optimal sensor placement and model updating applied to the operational modal analysis of a nonuniform wind turbine tower

IF 1 4区 工程技术 Q4 ENGINEERING, MECHANICAL
Mohammad Tamizifar, M. Mosayebi, S. Ziaei-Rad
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引用次数: 0

Abstract

Abstract. Test planning is a crucial step in the operational modal analysis (OMA) of wind turbines (WT), and it is an essential part of choosing the best positions for installing the sensors of the structures. On the other hand, updating the finite element model (FEM) with the OMA results implies a better prediction of the real structure's dynamic and vibrational behavior. This paper aims to show how the OMA of a nonuniform and two-section wind turbine tower can be performed more effectively, using the required test planning and optimal sensor placement. Then, accordingly, the OMA is used in operating and parked conditions to find the objective bending mode characteristics. Moreover, the updating of the applicable FEM of the multi-sectional wind turbine tower will be described. The tailor-made genetic algorithm (GA) is used to find the MEMS (micro electro-mechanical system) sensors' optimal positions of the WT under study. The OMA was performed and the acquired data analyzed using the stochastic subspace identification (SSI) method. Based on the OMA results, the FEM is updated by applying the sensitivity method. The results show that a tailor-made GA is a practical and quick approach to finding the optimal position of the sensors to obtain the best results for the objective modes of the WT. The OMA results, under operating and parked conditions, prove some modal characteristics of WTs. Based on the sensitivity analysis and engineering judgment, the modulus of elasticity was selected as a parameter for updating. Finally, we found that the updated FEM had less than 1 % error compared to the obtained frequencies from the test.
传感器优化配置与模型更新应用于非均匀风力发电塔架运行模态分析
摘要试验规划是风力发电机组运行模态分析(OMA)的关键步骤,是选择结构传感器最佳安装位置的重要组成部分。另一方面,用OMA结果更新有限元模型(FEM)意味着更好地预测实际结构的动力和振动行为。本文旨在展示如何使用所需的测试计划和最佳传感器放置,更有效地执行非均匀两段风力涡轮机塔的OMA。然后,将该模型应用于车辆运行和停放工况,求出目标弯曲模态特征。此外,还介绍了多截面风力机塔架适用有限元法的更新。利用定制遗传算法(GA)找到所研究的微机电系统传感器的最优位置。使用随机子空间识别(SSI)方法对获取的数据进行分析。在此基础上,采用灵敏度法对有限元进行了修正。结果表明,定制遗传算法是一种实用且快速的方法,可以找到传感器的最佳位置,从而获得小波目标模式的最佳结果。在运行和停车条件下的OMA结果证明了小波的一些模态特征。基于灵敏度分析和工程判断,选取弹性模量作为更新参数。最后,我们发现更新的FEM与从测试中获得的频率相比误差小于1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mechanical Sciences
Mechanical Sciences ENGINEERING, MECHANICAL-
CiteScore
2.20
自引率
7.10%
发文量
74
审稿时长
29 weeks
期刊介绍: The journal Mechanical Sciences (MS) is an international forum for the dissemination of original contributions in the field of theoretical and applied mechanics. Its main ambition is to provide a platform for young researchers to build up a portfolio of high-quality peer-reviewed journal articles. To this end we employ an open-access publication model with moderate page charges, aiming for fast publication and great citation opportunities. A large board of reputable editors makes this possible. The journal will also publish special issues dealing with the current state of the art and future research directions in mechanical sciences. While in-depth research articles are preferred, review articles and short communications will also be considered. We intend and believe to provide a means of publication which complements established journals in the field.
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