Structural Health Monitoring of Multi-MW-Scale Wind Turbines by Non-contact Optical Measurement Techniques: An Application on a 2.5-MW Wind Turbine

2015 ◽  
pp. 125-136
Author(s):  
Muammer Ozbek ◽  
Daniel J. Rixen
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Optical Measurement ◽  
Measurement Techniques ◽  
Structural Health

Structural Health Monitoring of Composite Material Typical of Wind Turbine Blades by Novelty Detection on Vibration Response

2012 ◽  
Vol 518 ◽  
pp. 319-327
Author(s):  
Nikolaos Dervilis ◽  
R. Barthorpe ◽  
Wieslaw Jerzy Staszewski ◽  
Keith Worden
Keyword(s):  
Composite Material ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Novelty Detection ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Structural Health

New generations of offshore wind turbines are playing a leading role in the energy arena. One of the target challenges is to achieve reliable Structural Health Monitoring (SHM) of the blades. Fault detection at the early stage is a vital issue for the structural and economical success of the large wind turbines. In this study, experimental measurements of Frequency Response Functions (FRFs) are used and identification of mode shapes and natural frequencies is accomplished via an LMS system. Novelty detection is introduced as a robust statistical method for low-level damage detection which has not yet been widely used in SHM of composite blades. Fault diagnosis of wind turbine blades is a challenge due to their composite material, dimensions, aerodynamic nature and environmental conditions. The novelty approach combined with vibration measurements introduces an online condition monitoring method. This paper presents the outcomes of a scheme for damage detection of carbon fibre material in which novelty detection approaches are applied to FRF measurements. The approach is demonstrated for a stiffened composite plate subject to incremental levels of impact damage.

Operational Model Updating of Low-Order HAWT Models for Structural Health Monitoring Applications

2013 ◽  
Author(s):  
Antonio Velazquez ◽  
R. Andrew Swartz
Keyword(s):  
Global Optimization ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Model Updating ◽  
Structural Health ◽  
The Past ◽  
Operation Conditions ◽  
Computer Aided

For the past decade, wind turbines have become the largest source of installed renewable-energy capacity in the United States. Economical, maintenance and operation are critical issues when dealing with such large slender structures, particularly when these structures are sited remotely. Because of the chaotic nature of non-stationary rotating-machinery systems such as the horizontal-axis wind turbines (HAWTs), in-operation modeling and computer-aided numerical characterization is typically troublesome, and tends to be imprecise while predicting the real content of the actual aerodynamic loading. Loading environment under operation conditions is usually substantially different from those driven by modal testing or computer-aided model characterization and difficult to measure directly in the field. In addition, rotational machinery such as HAWTs exhibit complex and nonlinear dynamics (i.e., precession and Coriolis effects, torsional coupling, nonlinear geometries, plasticity of composite materials); and are subjected to nonlinear constrained conditions (i.e., aeroelastic interaction). For those reasons, modal-aeroelastic and computer-aided models reproduced under controlled conditions may fail to predict the correct non-stationary loading and resistance patterns of wind turbines in actual operation. Operational techniques for extracting modal properties under actual non-stationary loadings are needed in order to (1) improve computer-aided elasto-aerodynamic models to better characterize the actual behavior of HAWTs in operational scenarios, (2) improve and correlate models, (3) monitor and diagnose the system for integrity and damage through time, or even (4) optimize control systems. For structural health monitoring (SHM) applications, model updating of stochastic aerodynamic problems has gained interest over the past decades. For situations where optimizing objective functions are not differentiable, convex or continuous in nature that is the case of gradient methods such as Modal Assurance Criterion (MAC), global optimization (metaheurstic) methods based on probability principles have emerged. These search engine techniques are promising suitable to cope with non-stationary-stochastic system identification methods for model updating of HAWT systems. A probability theory framework is employed in this study to update the wind turbine model using such a stochastic global optimization approach. Structural identification is addressed under regular wind turbine operation conditions for non-stationary, unmeasured, and uncontrolled excitations by means of the eigensystem realization theory (ERA). This numerical framework is then tied up with an adaptive simulated annealing (ASA) numerical engine for solving the problem of model updating. Numerical results are presented for an experimental deployment of a small HAWT structure. Results are benchmarked and validated with other empirical mode-decomposition and time-domain solutions.

Structural Health Monitoring of Research-Scale Wind Turbine Blades

2013 ◽  
Vol 558 ◽  
pp. 364-373 ◽  
Author(s):  
Stuart G. Taylor ◽  
Kevin M. Farinholt ◽  
Gyu Hae Park ◽  
Charles R. Farrar ◽  
Michael D. Todd ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Embedded Systems ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Field Tests ◽  
Ultrasonic Waves ◽  
Wind Turbine Blades ◽  
Structural Health ◽  
Sensing System

This paper presents ongoing work by the authors to implement real-time structural health monitoring (SHM) systems for operational research-scale wind turbine blades. The authors have been investigating and assessing the performance of several techniques for SHM of wind turbine blades using piezoelectric active sensors. Following a series of laboratory vibration and fatigue tests, these techniques are being implemented using embedded systems developed by the authors. These embedded systems are being deployed on operating wind turbine platforms, including a 20-meter rotor diameter turbine, located in Bushland, TX, and a 4.5-meter rotor diameter turbine, located in Los Alamos, NM. The SHM approach includes measurements over multiple frequency ranges, in which diffuse ultrasonic waves are excited and recorded using an active sensing system, and the blades global ambient vibration response is recorded using a passive sensing system. These dual measurement types provide a means of correlating the effect of potential damage to changes in the global structural behavior of the blade. In order to provide a backdrop for the sensors and systems currently installed in the field, recent damage detection results for laboratory-based wind turbine blade experiments are reviewed. Our recent and ongoing experimental platforms for field tests are described, and experimental results from these field tests are presented. LA-UR-12-24691.

Prediction of wind turbine blade trailing edge noise under various flow conditions for a passive damage detection system

2021 ◽  
Vol 263 (2) ◽  
pp. 4079-4087
Author(s):  
Murat Inalpolat ◽  
Caleb Traylor
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Detection System ◽  
Wind Turbine Blade ◽  
Flow Conditions ◽  
Trailing Edge ◽  
Structural Health ◽  
Acoustic Spectra

Noise generated by turbulent boundary layer over the trailing edge of a wind turbine blade under various flow conditions is predicted and analyzed for structural health monitoring purposes. Wind turbine blade monitoring presents a challenge to wind farm operators, and an in-blade structural health monitoring system would significantly reduce O&M costs. Previous studies into structural health monitoring of blades have demonstrated the feasibility of designing a passive detection system based on monitoring the flow-generated acoustic spectra. A beneficial next step is identifying the robustness of such a system to wind turbine blades under different flow conditions. To examine this, a range of free stream air velocities from 5 m/s to 20 m/s and a range of rotor speeds from 5 rpm to 20 rpm are used in a reduced-order model of the flow-generated sound in the trailing edge turbulent boundary layer. The equivalent lumped acoustics sources are predicted based on the turbulent flow simulations, and acoustic spectra are calculated using acoustic ray tracing. Each case is evaluated based on the changes detected when damage is present. These results can be used to identify wind farms that would most benefit from this monitoring system to increase efficiency in deployment of turbines.

scholarly journals Structural health monitoring of wind turbines: method and application to a HAWT

Wind Energy ◽  
2011 ◽  
Vol 14 (4) ◽  
pp. 603-623 ◽  
Author(s):  
Douglas Adams ◽  
Jonathan White ◽  
Mark Rumsey ◽  
Charles Farrar
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Structural Health

Radar-based structural health monitoring of wind turbine blades: The case of damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Jochen Moll ◽  
Philip Arnold ◽  
Moritz Mälzer ◽  
Viktor Krozer ◽  
Dimitry Pozdniakov ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Turbine Blades ◽  
Heterogeneous Material ◽  
Material System ◽  
Wind Turbine Blades ◽  
Millimetre Wave ◽  
Structural Health

Structural health monitoring of wind turbine blades is challenging due to its large dimensions, as well as the complex and heterogeneous material system. In this article, we will introduce a radically new structural health monitoring approach that uses permanently installed radar sensors in the microwave and millimetre-wave frequency range for remote and in-service inspection of wind turbine blades. The radar sensor is placed at the tower of the wind turbine and irradiates the electromagnetic waves in the direction of the rotating blades. Experimental results for damage detection of complex structures will be presented in a laboratory environment for the case of a 10-mm-thick glass-fibre-reinforced plastic plate, as well as a real blade-tip sample.

Passive Structural Health Monitoring Applied to a Wind Turbine Model

2009 ◽  
Author(s):  
Kyle Bassett ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Large Scale ◽  
Residual Error ◽  
Series Data ◽  
Vibration Signals ◽  
Structural Health ◽  
Monitoring Technique

Structural health monitoring is a technique devised to monitor the structural conditions of a system in an attempt to take corrective measures before the system fails. A passive structural health monitoring technique is presented, which serves to leverage historic time series data in order to both detect and localize damage on a wind turbine blade aerodynamic model. First, vibration signals from the healthy system are recorded for various input conditions. The data is normalized and auto-regressive (AR) coefficients are determined in order to uniquely identify the normal behavior of the system for each input condition. This data is then stored in a healthy state database. When the structural condition of the system is unknown the vibration signals are acquired, normalized and identified by their AR coefficients. Damage is detected through the residual error which is calculated as the difference between the AR coefficients of the unknown and healthy structural conditions. This technique is tailored for wind turbines and the application of this approach is demonstrated in a wind tunnel using a small turbine blade held with four springs to create a dual degree-of-freedom system. The vibration signals from this system are characterized by free-stream speed. Damage is replicated through mass addition on each of the blades ends and is located by an increase in residual error from the accelerometer mounted closest to the damaged area. The outlined procedure and demonstration illustrate a single stage structural health monitoring technique that, when applied on a large scale, can avoid catastrophic turbine disasters and work to effectively reduce the maintenance costs and downtime of wind farm operations.

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