Identification Uncertainties of Bending Modes of an Onshore Wind Turbine for Vibration-Based Monitoring

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Clemens Jonscher, Sören Möller, Leon Liesecke, Daniel Schuster, Benedikt Hofmeister, Tanja Grießmann, Raimund Rolfes
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Abstract

This study considers the identification uncertainties of closely spaced bending modes of an operating onshore concrete-steel hybrid wind turbine tower. The knowledge gained contributes to making mode shapes applicable to wind turbine tower monitoring rather than just mode tracking. One reason is that closely spaced modes make it difficult to determine reliable mode shapes for them. For example, the well-known covariance-driven stochastic subspace identification (SSI-COV) yields complex mode shapes with multiple mean phases in the complex plane, which does not allow error-free transformation to the real space. In contrast, the Bayesian Operational Modal Analysis (BAYOMA) allows the determination of real mode shapes. The application of BAYOMA presents a further challenge when quantifying the associated uncertainties, as the typical assumption of a linear, time-invariant system is violated. Therefore, validity is not self-evident and a comprehensive investigation and comparison of results is required. It has already been shown in a previous study that the significant part of the uncertainty in the mode shapes corresponds to their orientation in the mode subspace (MSS). Despite all the challenges mentioned, there is still a great need to develop reliable monitoring parameters (MPs) for Structural Health Monitoring (SHM). This study contributes to this by analysing metrics for comparing mode shapes. In addition to the well-known Modal Assurance Criteria (MAC), the Second-Order MAC (S2MAC) is also used to eliminate the alignment uncertainty by comparing the mode shape with a MSS. In addition, the mode shape identification uncertainties of BAYOMA are also considered. Including uncertainties is also essential for the typically used natural frequencies and damping ratios, which can be more appropriately used if the identification uncertainty is known.

Abstract Image

基于振动监测的陆上风力涡轮机弯曲模态识别不确定性
本研究考虑了正在运行的陆上混凝土-钢混合风力涡轮机塔架的紧密间隔弯曲模式的识别不确定性。所获得的知识有助于将模态振型应用于风力涡轮机塔架监测,而不仅仅是模态跟踪。其中一个原因是,由于模态间距较近,很难确定其可靠的模态振型。例如,众所周知的协方差驱动随机子空间识别(SSI-COV)会产生复平面上具有多个平均相位的复杂模态振型,无法将其无差错地转换到真实空间。相比之下,贝叶斯运行模态分析(BAYOMA)可以确定真实的模态形状。贝叶斯运行模态分析法的应用在量化相关的不确定性时提出了进一步的挑战,因为它违反了线性、时间不变系统的典型假设。因此,有效性并不是不言而喻的,需要对结果进行全面的调查和比较。之前的一项研究已经表明,模式形状的不确定性主要与它们在模式子空间(MSS)中的方向有关。尽管存在上述挑战,但仍亟需为结构健康监测(SHM)开发可靠的监测参数(MPs)。本研究通过分析用于比较模态振型的指标,对此做出了贡献。除了众所周知的模态保证准则 (MAC),还采用了二阶 MAC (S2MAC),通过将模态振型与 MSS 进行比较来消除对齐不确定性。此外,还考虑了 BAYOMA 的模形识别不确定性。对于通常使用的固有频率和阻尼比来说,将不确定性包括在内也是至关重要的。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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