Operational Modal Analysis of Offshore Wind Turbine Tower under Ambient Excitation

Zhang, Peng; He, Zhengjie; Cui, Chunyi; Ren, Liang; Yao, Ruqing

doi:10.3390/jmse10121963

Cited by 7 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, scouring decreases the buried depth of the OWT foundation; accordingly, structural dynamic properties change due to the scouring. Thus, structural dynamic characteristics (e.g., frequency, the damping ratio, and mode shapes) are employed as an indicator of the scouring [60][61][62]. Weijtjens et al (2017) analyzed the modal parameters of OWTs and found that the resonance frequency of the second-order mode is most closely related to OWT scouring [62].…”

Section: Scouringmentioning

confidence: 99%

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

Yang,

Liang,

Zhu

et al. 2024

JMSE

View full text Add to dashboard Cite

The service environment of offshore wind turbine (OWT) support structures is harsh, and it is extremely difficult to replace these structures during their operational lifespan, making their failure a catastrophic event. The structural health monitoring (SHM) of OWT support structures is a crucial aspect of operational maintenance for OWT support structures, aiming to mitigate significant financial losses. This paper systematically summarizes the current monitoring methods and technologies for OWT support structures, including towers and foundations. Through the review of monitoring content and the evolution of monitoring techniques for supporting structures, it delves deeper into the challenges faced by wind turbine monitoring and highlights potential avenues for future development. Then, the current damage identification techniques for OWT towers and foundations are analyzed, exploring various methods including model-based, vibration-based, artificial intelligence and hybrid fault diagnosis methods. The article also examines the advantages and disadvantages of each approach and outlines potential future directions for research and development in this field. Furthermore, it delves into the current damage identification techniques for OWT towers and foundations, discussing prevalent challenges and future directions in this domain. This status review can provide reference and guidance for the monitoring design of OWT support structures, and provide support for the fault diagnosis of OWT support structures.

show abstract

Section: Scouringmentioning

confidence: 99%

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

Yang,

Liang,

Zhu

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Zhang et al [53] proposed an RDT-SSI method to estimate the operational frequency of an OWT under ambient excitation using combined Kalman filter, the Random Decrement Technique (RDT), and stochastic subspace identification (SSI) methods. RDT technique used to pick up the free-timedecaying signal of a structure having unknown excitation and SSI estimate the modal properties of large-scale turbine having high noise level.…”

Section: B Numerical Simulationsmentioning

confidence: 99%

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Memari,

Shakya,

Shekaramiz

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The increasing demand for wind power requires more frequent inspections to identify defects in the Wind Turbine Blades (WTBs). These defects, if not detected, can compromise the structural integrity and safety of wind turbines. As WTBs are crucial and costly components, they may suffer material degradation and fatigue failure, which affects their performance and safety. Thus, the urgency for efficient and regular monitoring to maintain their structural integrity is greater than ever. This review paper explores innovative methods in fatigue testing, damage detection, and structural reliability in WTBs, focusing on the use of recent inspection methods, including those that take advantage of drones. Drones are used to identify defects such as cracks, erosion, and coating irregularities using high-resolution imagery with the onboard cameras. Various investigators have developed novel data-driven approaches, incorporating machine learning and deep learning, to accurately identify these defects. Although deep learning-based image processing has been successful in other public infrastructure contexts, its application to wind turbine inspection from aerial images presents unique challenges. This paper also highlights the critical role of failure inspection in enhancing the operational integrity of WTBs, showcasing state-of-the-art deep learning techniques that are pivotal for identifying and analyzing failures in WTBs from images captured by drones. The paper provides insights into the latest developments in using drone imagery for blade defect detection, contrasting this method with traditional non-destructive techniques. This approach could significantly transform the wind energy industry by offering a more efficient, automated, and precise way of ensuring the structural health of wind turbines. Unlike previous studies that predominantly focus on isolated aspects such as inspection or fatigue, this review paper not only integrates the three major aspects of WTBs integrity in terms of aerial inspection, image processing using machine learning, and structural integrity of the blade but also undertakes an extensive examination of the prevailing methodologies in the field, pinpointing crucial gaps and challenges. It provides a detailed review of existing research, covering various areas including automated inspection, image processing techniques, fatigue analysis, and the reliability of wind turbines. This approach enriches the discourse by offering a multifaceted perspective on WTB maintenance, thereby advancing the understanding of operational integrity within the field of wind energy.

show abstract

“…On the contrary, techniques based on stochastic subspace identification (SSI) are well-known input-free methods used to extract modal parameters under ambient excitations. In other words, SSI-based identification techniques can conduct operational modal analysis (OMA) under the assumption of a stationary process using output-only measurements and have numerous applications across the monitoring of bridges, wind turbine towers, and power systems [4][5][6]. The stability and robustness of SSI for noise contamination have been proven [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Tracking Modal Parameters of Structures Online Using Recursive Stochastic Subspace Identification under Ambient Excitations

Huang,

Chen,

Weng

et al. 2024

Buildings

View full text Add to dashboard Cite

Continuous and autonomous system identification is an alternative to regular inspection during operations, which is essential for structural integrity management (SIM) as well as structural health monitoring (SHM). In this regard, online (or real-time) system identification techniques that have recently received considerable attention can be used to assess the current condition and performance during operations and, in the meantime, can be utilized to detect any damage or deterioration. For example, stochastic subspace identification (SSI), based on recursive formulation, has proven its capability in tracking modal parameters as well as time-variant dynamic behaviors. This study proposes the implementation of recursive SSI (RSSI) using the matrix inversion lemma to track slow time-varying parameter changes under ambient excitations. Subsequently, some investigations for practical implementation are examined and discussed. For verifying the reliability of SHM applications based on the proposed methods, two datasets measured from different experiments are exploited to identify the modal parameters reclusively. The results from both numerical simulations and experimental investigations demonstrated the effectiveness of tracking the modal parameters exhibiting time-varying dynamic characteristics under white noise excitations (or ambient excitations).

show abstract

Operational Modal Analysis of Offshore Wind Turbine Tower under Ambient Excitation

Cited by 7 publications

References 41 publications

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Tracking Modal Parameters of Structures Online Using Recursive Stochastic Subspace Identification under Ambient Excitations

Contact Info

Product

Resources

About