Applicability Analysis of Inspection and Monitoring Technologies in Wind Turbine Towers

Yue, Yanchao; Tian, Jingjing; Bai, Yongtao; Jia, Kai; Jian, He; Luo, Dong; Chen, Tangbing

doi:10.1155/2021/5548727

Cited by 6 publications

(3 citation statements)

References 36 publications

(31 reference statements)

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“…Acoustic Emission (AE) is a non-destructive material testing method based on detecting the transient elastic waves in a sudden redistribution of stress in a material (Faisal et al, 2017;Yue et al, 2021). Energy released by AE localized sources (e.g., crack, slip or dislocation movements, or phase transformations in metals) propagates through test material which is received by the AE sensor.…”

Section: Corrosion Detection Techniques and Sensorsmentioning

confidence: 99%

Review of corrosion monitoring and prognostics in offshore wind turbine structures: Current status and feasible approaches

Brijder

Hagen²,

Cortés³

et al. 2022

Front. Energy Res.

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As large wind farms are now often operating far from the shore, remote condition monitoring and condition prognostics become necessary to avoid excessive operation and maintenance costs while ensuring reliable operation. Corrosion, and in particular uniform corrosion, is a leading cause of failure for Offshore Wind Turbine (OWT) structures due to the harsh and highly corrosive environmental conditions in which they operate. This paper reviews the state-of-the-art in corrosion mechanism and models, corrosion monitoring and corrosion prognostics with a view on the applicability to OWT structures. Moreover, we discuss research challenges and open issues as well strategic directions for future research and development of cost-effective solutions for corrosion monitoring and prognostics for OWT structures. In particular, we point out the suitability of non-destructive autonomous corrosion monitoring systems based on ultrasound measurements, combined with hybrid prognosis methods based on Bayesian Filtering and corrosion empirical models.

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Section: Corrosion Detection Techniques and Sensorsmentioning

confidence: 99%

Review of corrosion monitoring and prognostics in offshore wind turbine structures: Current status and feasible approaches

Brijder

Hagen²,

Cortés³

et al. 2022

Front. Energy Res.

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“…Wind turbine assembly facilities are typically located in remote mountainous and offshore regions abundant in wind energy resources to ensure stable wind farm operations [9]. However, challenges arise in the daily monitoring and maintenance of wind turbines due to factors like installation, transportation, and environmental conditions [10]. Given the complexity of the operating environment and the diversity of components involved in the drive chain, effectively addressing the fault issues of wind turbine transmission chains is critical to ensure their reliability and power generation efficiency [11].…”

Section: Introductionmentioning

confidence: 99%

Multidimensional clustering based fault diagnosis method for wind turbine transmission chain

Zhang,

Ruan,

Huang

et al. 2023

Meas. Sci. Technol.

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With the rapid development of wind power generation, ensuring the reliability and fault diagnosis of wind turbine transmission chains has become a focal point. Traditional clustering methods that address the fault diagnosis problem in wind turbine transmission chains suffer from low fault discrimination and accuracy, as they rely on a single indicator. This paper proposes a two-dimensional clustering model that utilizes multiple indicators to address the issue of distinguishing similar faults within the wind turbine transmission chain from various perspectives and dimensions. The proposed approach involves collecting vibration signals from the transmission chain of wind turbines using sensors and using the root mean square and kurtosis of the fault signal as clustering features. A multidimensional polar coordinate clustering model is established, with the signal kurtosis representing the polar angle and the root mean square representing the polar radius. The clustering of fault signals within wind turbine transmission chains is achieved by optimizing the clustering boundaries by establishing a clustering boundary optimization equation. The results of the developed clustering model are evaluated using external and internal evaluation methods. Experimental results demonstrate that this method exhibits high accuracy and low computational complexity in diagnosing faults within wind turbine transmission chains. In comparison with other clustering methods, the proposed method outperforms them according to the experimental results. Hence, this study presents a novel approach for health monitoring and fault diagnosis of the transmission chain of wind turbines, which has significant implications for improving the reliability of wind turbine operation and reducing maintenance costs.

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“…Each of the three methods has its advantages and disadvantages. Ultrasonic phased array detection is more suitable for structures that are difficult to access, has limitations in repeatability accuracy, and finds is difficult to calculate the defects quantitatively; TOFD detection can achieve the quantified calculation of flaws, and has blind spots in surface detection; the magnetic memory method is cost-effective, yet the signal it generates is relatively feeble and susceptible to various influencing factors [43]. Farhan et al (2022) studied the optimal monitoring time and repair strategy for OWT weld from the early stage of its service life.…”

mentioning

confidence: 99%

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

Yang,

Liang,

Zhu

et al. 2024

JMSE

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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.

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Applicability Analysis of Inspection and Monitoring Technologies in Wind Turbine Towers

Cited by 6 publications

References 36 publications

Review of corrosion monitoring and prognostics in offshore wind turbine structures: Current status and feasible approaches

Review of corrosion monitoring and prognostics in offshore wind turbine structures: Current status and feasible approaches

Multidimensional clustering based fault diagnosis method for wind turbine transmission chain

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

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