Evaluation of NASA PZT Sensor/Actuator for Structural Health Monitoring of a Wind Turbine Blade

Zayas, Jose R.; Paquette, Joshua; Werlink, Rudolph J.

doi:10.2514/6.2007-1020

Cited by 8 publications

(4 citation statements)

References 3 publications

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“…Some of the previous work in this area is briefly reviewed. NASA 14 developed an impedance-based SHM system by PZT sensors and actuators. The SHM system can detect damage inside a blade but a sensor must be placed close to the damage.…”

Section: A Health Monitoring Of Wind Turbine Bladesmentioning

confidence: 99%

Wireless Structural Health Monitoring of Wind Turbine Blades Using an Energy Harvester as a Sensor

Lim

Mantell

Seiler

2014

32nd ASME Wind Energy Symposium

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Section: A Health Monitoring Of Wind Turbine Bladesmentioning

confidence: 99%

Wireless Structural Health Monitoring of Wind Turbine Blades Using an Energy Harvester as a Sensor

Lim

Mantell

Seiler

2014

32nd ASME Wind Energy Symposium

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“…A variety of sensing approaches have been considered for structural health monitoring of composite structures such as helicopter or wind turbine blades including: acoustic sensors, accelerometers, strain gauges, piezoceramic transducers and fiber optic sensors. [4][5][6][7][8] These sensors typically provide strain, acceleration and acoustic/vibration data in real time. Sensor data is processed using algorithms designed to predict the location and extent of damage based on sensor data.…”

Section: A Sensors and Telemetry For Structural Health Monitoringmentioning

confidence: 99%

Wind Turbine Blades as a Strain Energy Source for Energy Harvesting

Lim

Mantell

Seiler³

et al. 2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Structural health monitoring of wind turbine blade mechanical performance can inform maintenance decisions, lead to reduced down time and improve the reliability of wind turbines. Wireless, self-powered strain gages and accelerometers have been proposed to transmit blade data to a monitoring system located in the nacelle. Each sensor node is powered by a strain Energy Harvester (EH). The amplitude and frequency of strain at the blade surface (where the EH is mounted) must be sufficient to enable data transfer. In this study, the strain energy available for energy harvesting is evaluated for three typical wind turbines with different wind conditions. A FAST simulation code, available through the National Renewable Energy Lab (NREL), is used to determine bending moments in the wind turbine blade. Given the moment data as a function of position along the blade and time (i.e. blade rotational position), strain in the blade is calculated. The data provide guidance for optimal design of the energy harvester.

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“…The root mean square impendence response over a range of frequencies was correlated to changes in the local stiffness of the structure. Others (Zayas et al, 10 Light-Marquez et al, 11 Deines et al 12 ) have developed systems comprised of groups of PZT patches: a single patch is an actuator and multiple, nearby patches are sensors. Damage is detected by one of several signal processing approaches, such as time series analysis and frequency transfer functions, that consider the sensor responses given the input excitation.…”

Section: Introductionmentioning

confidence: 99%

Wireless monitoring algorithm for wind turbine blades using Piezo‐electric energy harvesters

2016

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Wind turbine blade failure can be catastrophic and lead to unexpected power interruptions. In this paper, a Structural Health Monitoring (SHM) algorithm is presented for wireless monitoring of wind turbine blades. The SHM algorithm utilizes accumulated strain energy data, such as would be acquired by piezoelectric materials. The SHM algorithm compares the accumulated strain energy at the same position on the three blades. This exploits the inherent triple redundancy of the blades and avoids the need for a structural model of the blade. The performance of the algorithm is evaluated using probabilistic metrics such as detection probability (True Positive) and false alarm rate (False Positive). The decision time is chosen to be sufficiently long that a particular damage level can be detected even in the presence of system sensor noise and wind variations. Finally, the proposed algorithm is evaluated with a case study of a utility-scale turbine. The noise level is based on measurements acquired from strain sensors mounted on the blades of a Clipper Liberty C96 turbine. Strain energy changes associated with damage from matrix cracking and delamination are simulated with a finite element model. The case study demonstrates that the proposed algorithm can detect damage with a high probability based on a decision time period of approximately 50-200 days. NOMENCLATUREN B D Average of strain energy one-step increment, [ J/step] B k D Strain energy one-step increment, [ J/step] E D Young's Modulus of a composite blade, [GPa] E 0 D Degraded Young's modulus, [GPa] E EH D Young's modulus of an energy harvester, [GPa] g D D Degradation parameter, [-] k d D Decision time, [step] k f D Last time step of the given time frame, [step] k s D Statistical evaluation time, [step] m D Number of divided windows from the evaluation time, [-] n D Integer, [-] p FP D Possibility of False Positive, [%] p TP D Possibility of True Positive, [%] r .ij/ k D Residual of i, j blades at time k, [ J] T D Threshold, [ J] v D Variational term in B k , [ J] V D Volume of energy harvester, OEm 3 W .i/ k D Strain energy of the EH in the blade i, [-] t D Step time, [sec/step] ı k D Random Variable in the residual, [ J] D Strain of a composite blade, [ -strain] 0 D Strain of a damaged composite blade, [ -strain] 2 D Variance of ı k , [ J 2 ] 2 k D Variance of r k , [ J 2 ]

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Evaluation of NASA PZT Sensor/Actuator for Structural Health Monitoring of a Wind Turbine Blade

Cited by 8 publications

References 3 publications

Wireless Structural Health Monitoring of Wind Turbine Blades Using an Energy Harvester as a Sensor

Wireless Structural Health Monitoring of Wind Turbine Blades Using an Energy Harvester as a Sensor

Wind Turbine Blades as a Strain Energy Source for Energy Harvesting

Wireless monitoring algorithm for wind turbine blades using Piezo‐electric energy harvesters

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