Modal Parameters Estimation of an Offshore Wind Turbine Using Measured Acceleration Signals from the Drive Train

El-Kafafy, Mahmoud; Colanero, L.; Gioia, Nicoletta; Devriendt, Christof; Guillaume, Patrick; Helsen, Jan

doi:10.1007/978-3-319-54109-9_5

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Due to the extreme environment in which wind turbines operate, machines and components are subjected to highly variable operating conditions that cause important mechanical stresses [8]. For this reason, the drivetrain has been already analized for long periods as described in References [9,10]. The referenced works, however, analyze the drivetrain in stand still condition.…”

Section: Field Data-driven Designmentioning

confidence: 99%

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Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

et al. 2019

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Noise, vibration and harshness (NVH) problems are critical issues to be tackled for wind turbine drivetrains. Tracking the behavior of modal parameters of the machines’ fundamental modes during operation it is of high interest to validate complex simulation models. A powerful approach for this purpose is represented by operational modal analysis (OMA). This paper describes the investigation of an automated technique for continuously tracking the modes of a rotating mechanical system running in normal operating conditions. The modal estimation procedure is based on an automatic version of the pLSCF (poly-reference Least-Square Complex Frequency-Domain) algorithm. The latter is coupled with a method that automatically tracks the modal parameters along different data sets. The use of OMA on a rotating component of the wind turbine creates the need to deal with harmonics in order to satisfy one of the assumptions of OMA. For this purpose, the use of a cepstrum editing procedure is analyzed and implemented. Modal estimates obtained from an automated analysis on stand still data and normal operating conditions data are compared, to test the added value of the cepstrum editing procedure and the robustness of the method when used on real data. To illustrate and validate the implemented methodology, data acquired during a long-term monitoring campaign of a wind turbine drivetrain are used.

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Section: Field Data-driven Designmentioning

confidence: 99%

“…The tracking procedure has been implemented as a comparison between the modal parameters estimated for each data set with a reference set of modes, as investigated in Reference [9]. For the procedure, a reference data set is identified and the relative modal parameters are estimated.…”

Section: Automatic Modal Parameters Trackingmentioning

confidence: 99%

Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

et al. 2019

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“…Still the wireless sensors need to feature a high resolution to detect the low accelerations at low wind speed, even at low frequencies. For instance, first bending modes at offshore wind turbines can be significantly lower than 0.2 Hz [13].…”

Section: Introductionmentioning

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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Combining Machine Learning and Operational Modal Analysis Approaches to Gain Insights in Wind Turbine Drivetrain Dynamics

Gioia

Daems

Verstraeten

et al. 2019

Topics in Modal Analysis &Amp; Testing, Volume 8

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Modal Parameters Estimation of an Offshore Wind Turbine Using Measured Acceleration Signals from the Drive Train

Cited by 5 publications

References 3 publications

Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

Identification of Noise, Vibration and Harshness Behavior of Wind Turbine Drivetrain under Different Operating Conditions

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

Combining Machine Learning and Operational Modal Analysis Approaches to Gain Insights in Wind Turbine Drivetrain Dynamics

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