Monitoring of Wind Turbine Structures using Stationary Sensors and Short-term Optical Techniques

Botz, M.; Raith, Manuel; Emiroglu, Altug; Große, Christian U.

doi:10.12783/shm2017/14150

Cited by 5 publications

(5 citation statements)

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“…The sensors to be analyzed are measured with a sampling rate of 100 Hz that is further resampled to 0.1 Hz to focus on quasistatic states. The sampling rate of 100 Hz is adequate to analyze the structure's Eigenmodes up to the 3 rd bending mode at 3.3 Hz as they are analyzed in [1]. To address data imbalance, such as for lower resolution SCADA data, missing values are filled by propagating from the last valid observation to the next valid observation or, for yaw angle data, using a fixed gradient backward interpolation method [9].…”

Section: Data Setmentioning

confidence: 99%

“…Exploring RUL and accumulated damage to supporting structures of wind turbines is crucial for sustainable and cost-effective operation. [1] and [2] developed methods to analyze damage scenarios and RUL by monitoring acceleration and strain data supported by numerical simulations. [2] concludes that model uncertainties can be reduced by in-field NDT inspections.…”

Section: Introductionmentioning

confidence: 99%

“…The challenges of dynamic displacement calculation and frequency analysis in wind turbines are addressed in various studies. [1] and [8] focus on the difficulties in capturing dynamic displacements and conducting frequency analysis, critical for turbine maintenance and RUL estimation. Similarly, [9] and [10] investigate the measurability of structural damages in wind turbines, focusing on dynamic and static displacement and frequency analysis challenges using advanced measurement and data preprocessing techniques.…”

Section: Introductionmentioning

confidence: 99%

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Operational Sensor Adjustment for Structural Health Monitoring Systems in Wind Turbines using Machine Learning

Rupfle,

Wondra,

Nowack

2024

Preprint

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The digitalization in structural engineering has significantly amplified the importance of Structural Health Monitoring systems, especially in civil engineering and the renewable energy sector. A monitoring system allows for deep analysis of the structure's health state, can detect anomalies, and also allows for the determination of remaining useful life. Initially, after installation, a monitoring system needs adjustment so that it can be used for various analysis purposes. This paper presents three different methods to fully automatically adjust sensor offsets of tilt, acceleration, and strain data in wind turbine towers. In the first method, we make use of an initiated 360-degree nacelle rotation and its unbalanced rotor weight-induced tower bending. In the second method, uniaxial operational data is collected and binned to form a database for a univariate regression. A third method consists of a biaxial operational data set, allowing for a multivariate regression. The applications of the second and third approaches can be extended to any slender structure under environmental load. The presented workflow also allows for fully automated sensor orientation determination and fault detection due to long-term drift observation of sensor characteristics.

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Section: Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operational Sensor Adjustment for Structural Health Monitoring Systems in Wind Turbines using Machine Learning

Rupfle,

Wondra,

Nowack

2024

Preprint

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“…Zusätzlich werden Dehnungen in horizontaler und in 45°‐Richtung erfasst. An anderer Stelle wurden bereits nähere Informationen zum Entwurf des Messsystems sowie zu Validierungsmessungen veröffentlicht.…”

Section: Versuchsobjekt Und Messaufbauunclassified

Überwachung und Modellierung der Tragstruktur von Windenergieanlagen

Botz

Emiroglu

Osterminski

et al. 2020

Beton und Stahlbetonbau

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The basis for creating a digital twin is a suitable model with a close connection to reality in the form of measurement data. In this paper it is explained how model and measurement data of the support structure of a wind turbine can be obtained. The procedure is implemented on a real wind turbine with a concrete/steel hybrid tower. The monitoring includes the acquisition of vibrations, strains and temperature as well as the determination of the dynamic Young's modulus of the concrete structure by sound velocity measurements. For modeling, a finite element model with shell elements was chosen, which takes into account stiffening effects from static loads. A comparison between model and reality is the most important premise for the use of a digital twin: Therefore, a multi-stage model validation based on modal parameters and local material stresses is performed. The Digital Twin offers different usage scenarios; here one is carried out: the fatigue calculation and remaining useful life (RUL) estimation. For this purpose, material stresses at highly stressed positions are determined using measurement data and model. The results are currently still unrealistically high lifetimes for the concrete part. The results are based on relatively short (1 h, 24 h) stress time series. This could be one reason for the high values. It could also be an indication that fatigue is a non-critical load case for the concrete part.

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“…The monitoring system consists of several wire-based accelerometers (PCB 3713B112G from PCB Piezotronics), a seismometer, strain gauges and thermocouples. More details on the monitoring system can be found in [19][20][21]. The accelerometers are installed at the adapter platform and the nacelle platform, as well as at the inner tower wall at heights of 45 m and 112 m. The acceleration range of this wirebased sensor type is ± 2 g. As the measurement is running continuously, simultaneous data is available that can be compared to the wireless acceleration measurements.…”

Section: Measurement Campaign Of Wireless Sensor Nodes Second Hardwarmentioning

confidence: 99%

Wireless High-Resolution Acceleration Measurements for Structural Health Monitoring of Wind Turbine Towers

Wondra

Malek

Botz

et al. 2019

Data-Enabled Discov. Appl.

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Structural health monitoring (SHM) will be pivotal for safe and economic operation of wind turbines. Timely discovery of changes within the structure and means of prediction of required maintenance will reduce production costs of electricity and catastrophic failures. Long-term structural acceleration recording can support damage detection on turbine towers and document progression of fatigue. Conventional acceleration recordings are based on wired sensor nodes at fixed positions with privileged accessibility and electric power supply. However, such positions might be near vibration nodes and not necessarily experience the maximum vibration amplitude. Shifts in eigenfrequencies can be an indicator of changes in structural stiffness, hence damage, but also be caused by environmental effects, e.g., temperature. Damages generate local effects while the structure's vibration spectrum is a global evaluation. If a sensor is close to the location of damage, the probability of detection is increased. Wireless sensors powered by batteries are advantageous for this task as they are independent of cabling for power supply and data transmission. Such monitoring of turbine tower structures is not common in practice and requires new data-enabled techniques to discover deviations from the optimal way of wind turbine operation. This paper proposes a new approach using wireless high-resolution acceleration measurement sensor nodes, exploiting the vibration response of wind turbine towers. Influences of acceleration resolution and sensor node locations onto the accuracy of eigenfrequency determination are demonstrated. A comparison between acceleration recordings by wireless sensor nodes and their wired counterparts is presented to prove the equivalence of the wireless sensing method. Finally, new data compression techniques used with the sensor nodes are discussed to reduce wireless transmission to a minimum.

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Monitoring of Wind Turbine Structures using Stationary Sensors and Short-term Optical Techniques

Cited by 5 publications

References 0 publications

Operational Sensor Adjustment for Structural Health Monitoring Systems in Wind Turbines using Machine Learning

Operational Sensor Adjustment for Structural Health Monitoring Systems in Wind Turbines using Machine Learning

Überwachung und Modellierung der Tragstruktur von Windenergieanlagen

Wireless High-Resolution Acceleration Measurements for Structural Health Monitoring of Wind Turbine Towers

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