Predictions of Structural Testing Characteristics for Wind Turbine Blades

2009 ◽  
Author(s):  
Michael Desmond ◽  
Darris White
Keyword(s):  
Wind Turbine ◽  
Fatigue Testing ◽  
Turbine Blades ◽  
Structural Testing ◽  
Wind Turbine Blades ◽  
Static Testing ◽  
Extreme Load ◽  
Design Loads ◽  
Certification Requirements ◽  
Blade Loads

Static and fatigue structural testing of wind turbine blades provides manufacturers with quantitative details in order to improve designs and meet certification requirements. Static testing entails applying extreme load cases through a combination of winches and weights to determine the ultimate strength of the blade while fatigue testing entails applying the operating design loads through forced hydraulics or resonant excitation systems over the life cycle of the blade to determine durability. Recently, considerable efforts have been put forth to characterize the reactions of wind turbine blades during structural testing in order to develop load and deflection predictions for the next generation of blade test facilities. Incorporating years of testing experience with historical test data from several wind turbine blades, curve fits were developed to extrapolate properties for blades up to one hundred meters in length. Furthermore, conservative assumptions were employed to account for blade variations due to inconsistent manufacturing processes. In short, this paper will outline the predictions of wind turbine blade loads and deflections during static and fatigue structural testing.

Evaluation of dual-axis fatigue testing of large wind turbine blades

2011 ◽  
Vol 226 (7) ◽  
pp. 1693-1704 ◽  
Author(s):  
Peter R Greaves ◽  
Robert G Dominy ◽  
Grant L Ingram ◽  
Hui Long ◽  
Richard Court
Keyword(s):  
Service Life ◽  
Wind Turbine ◽  
Fatigue Testing ◽  
Turbine Blades ◽  
Bending Moment ◽  
Fatigue Tests ◽  
Simulation Software ◽  
Wind Turbine Blades ◽  
Moment Distribution ◽  
Large Wind

Full-scale fatigue testing is part of the certification process for large wind turbine blades. That testing is usually performed about the flapwise and edgewise axes independently but a new method for resonant fatigue testing has been developed in which the flapwise and edgewise directions are tested simultaneously, thus also allowing the interactions between the two mutually perpendicular loads to be investigated. The method has been evaluated by comparing the Palmgren–Miner damage sum around the cross-section at selected points along the blade length that results from a simulated service life, as specified in the design standards, and testing. Bending moments at each point were generated using wind turbine simulation software and the test loads were designed to cause the same amount of damage as the true service life. The mode shape of the blade was tuned by optimising the position of the excitation equipment, so that the bending moment distribution was as close as possible to the target loads. The loads were converted to strain–time histories using strength of materials approach, and fatigue analysis was performed. The results show that if the bending moment distribution is correct along the length of the blade, then dual-axis resonant testing tests the blade much more thoroughly than sequential tests in the flapwise and edgewise directions. This approach is shown to be more representative of the loading seen in service and can thus contribute to a potential reduction in the weight of wind turbine blades and the duration of fatigue tests leading to reduced cost.

scholarly journals PSO-BP Neural Network-Based Strain Prediction of Wind Turbine Blades

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 1889 ◽  
Author(s):  
Xin Liu ◽  
Zheng Liu ◽  
Zhongwei Liang ◽  
Shun-Peng Zhu ◽  
José A. F. O. Correia ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Turbine Blades ◽  
Full Scale ◽  
Wind Turbine Blade ◽  
Blade Design ◽  
Wind Turbine Blades ◽  
Static Testing ◽  
Bpnn Model

The full-scale static testing of wind turbine blades is an effective means to verify the accuracy and rationality of the blade design, and it is an indispensable part in the blade certification process. In the full-scale static experiments, the strain of the wind turbine blade is related to the applied loads, loading positions, stiffness, deflection, and other factors. At present, researches focus on the analysis of blade failure causes, blade load-bearing capacity, and parameter measurement methods in addition to the correlation analysis between the strain and the applied loads primarily. However, they neglect the loading positions and blade displacements. The correlation among the strain and applied loads, loading positions, displacements, etc. is nonlinear; besides that, the number of design variables is numerous, and thus the calculation and prediction of the blade strain are quite complicated and difficult using traditional numerical methods. Moreover, in full-scale static testing, the number of measuring points and strain gauges are limited, so the test data have insufficient significance to the calibration of the blade design. This paper has performed a study on the new strain prediction method by introducing intelligent algorithms. Back propagation neural network (BPNN) improved by Particle Swarm Optimization (PSO) has significant advantages in dealing with non-linear fitting and multi-input parameters. Models based on BPNN improved by PSO (PSO-BPNN) have better robustness and accuracy. Based on the advantages of the neural network in dealing with complex problems, a strain-predictive PSO-BPNN model for full-scale static experiment of a certain wind turbine blade was established. In addition, the strain values for the unmeasured points were predicted. The accuracy of the PSO-BPNN prediction model was verified by comparing with the BPNN model and the simulation test. Both the applicability and usability of strain-predictive neural network models were verified by comparing the prediction results with simulation outcomes. The comparison results show that PSO-BPNN can be utilized to predict the strain of unmeasured points of wind turbine blades during static testing, and this provides more data for characteristic structural parameters calculation.

scholarly journals GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1026 ◽  
Author(s):  
Zheng Liu ◽  
Xin Liu ◽  
Kan Wang ◽  
Zhongwei Liang ◽  
José A.F.O. Correia ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Back Propagation ◽  
Health Assessment ◽  
Prediction Method ◽  
Full Scale ◽  
Wind Turbine Blades ◽  
Static Test ◽  
Static Testing ◽  
Input Variables

This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.

The Application of Aeroelastic Analysis Output Load Distributions to Finite Element Models of Wind

2005 ◽  
Vol 29 (2) ◽  
pp. 153-168 ◽  
Author(s):  
Timothy J. Knill
Keyword(s):  
Finite Element ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Fe Model ◽  
Wind Turbine Blades ◽  
Fe Modelling ◽  
Load Distributions ◽  
Aeroelastic Analysis ◽  
Extreme Load ◽  
Application Techniques

The structural design of wind turbine blades is a rapidly evolving technology. Finite element (FE) modelling is used extensively by structural designers to assess the behaviour of wind turbine blades under operational and extreme load conditions. This paper develops a method of transferring aerodynamic and inertial loads from the aeroelastic analysis output to the FE model. Once a procedure is developed and verified, case studies are undertaken using an FE model of a 34m blade. Loads are applied using the newly developed method and various FE analysis results compared to the same blade analysed under more traditional load application techniques. The case study clearly demonstrates that the method of applying loads can influence some types of analysis results significantly.

A review of full-scale structural testing of wind turbine blades

2014 ◽  
Vol 33 ◽  
pp. 177-187 ◽  
Author(s):  
H.F. Zhou ◽  
H.Y. Dou ◽  
L.Z. Qin ◽  
Y. Chen ◽  
Y.Q. Ni ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Full Scale ◽  
Structural Testing ◽  
Wind Turbine Blades

Relating Turbulence to Wind Turbine Blade Loads: Parametric Study with Multiple Regression Analysis

1999 ◽  
Vol 121 (3) ◽  
pp. 156-161 ◽  
Author(s):  
T. Kashef ◽  
S. R. Winterstein
Keyword(s):  
Regression Analysis ◽  
Wind Turbine ◽  
Parametric Study ◽  
Turbine Blades ◽  
Low Frequency ◽  
Frequency Content ◽  
Wind Turbine Blades ◽  
Systematic Method ◽  
Highly Correlated ◽  
Blade Loads

Different wind parameters are studied to find a set that is most useful in estimating fatigue loads on wind turbine blades. The histograms of rainflow counted stress ranges are summarized through their first three statistical moments and regression analysis is used to estimate these moments in various wind conditions. A systematic method of comparing the ability of different wind parameters to estimate the moments is described and results are shown for flapwise loads on three HAWTs. In the case of two of these turbines, the stress ranges are shown to be highly correlated with a turbulence measure obtained by removing a portion of the low-frequency content of the wind.

Development of a Dual-Axis Phase-Locked Resonant Excitation Test Method for Fatigue Testing of Wind Turbine Blades

2011 ◽  
Author(s):  
Darris White ◽  
Michael Desmond ◽  
Waleed Gowharji ◽  
Jenna A. Beckwith ◽  
Kenneth J. Meierjurgen
Keyword(s):  
Wind Turbine ◽  
Fatigue Testing ◽  
Turbine Blades ◽  
Test Method ◽  
Resonant Excitation ◽  
Wind Loading ◽  
Cycle Phase ◽  
Testing Time ◽  
Wind Turbine Blades ◽  
Operation Conditions

Collaborative efforts between Embry-Riddle Aeronautical University (ERAU) and the National Renewable Energy Laboratories (NREL) have resulted in an innovative dual-axis phase-locked resonant excitation (PhLEX) test method for fatigue testing of wind turbine blades. The Dual-axis phase-locked test method has shown to provide more realistic load application as compared to wind loading experienced through field operation conditions. The current concepts involved exciting the blade at its fundamental edgewise natural frequency while applying a force in the flap direction at that same frequency. This advanced test method incorporates existing commercially available test hardware, known as the Universal Resonant Excitation (UREX), combined with an additional hydraulically actuated member to dynamically force the blade using adaptive algorithms and advanced control strategies in order to provide cycle-to-cycle phase control and decreased testing time. In short, this paper will outline the development of a finite element model for predicting performance and evaluation of the results.

An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades

2020 ◽  
Vol 19 (6) ◽  
pp. 1711-1725 ◽  
Author(s):  
Jaclyn Solimine ◽  
Christopher Niezrecki ◽  
Murat Inalpolat
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Fatigue Testing ◽  
Turbine Blades ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Structural Health

This article details the implementation of a novel passive structural health monitoring approach for damage detection in wind turbine blades using airborne sound. The approach utilizes blade-internal microphones to detect trends, shifts, or spikes in the sound pressure level of the blade cavity using a limited network of internally distributed airborne acoustic sensors, naturally occurring passive system excitation, and periodic measurement windows. A test campaign was performed on a utility-scale wind turbine blade undergoing fatigue testing to demonstrate the ability of the method for structural health monitoring applications. The preliminary audio signal processing steps used in the study, which were heavily influenced by those methods commonly utilized in speech-processing applications, are discussed in detail. Principal component analysis and K-means clustering are applied to the feature-space representation of the data set to identify any outliers (synonymous with deviations from the normal operation of the wind turbine blade) in the measurements. The performance of the system is evaluated based on its ability to detect those structural events in the blade that are identified by making manual observations of the measurements. The signal processing methods proposed within the article are shown to be successful in detecting structural and acoustic aberrations experienced by a full-scale wind turbine blade undergoing fatigue testing. Following the assessment of the data, recommendations are given to address the future development of the approach in terms of physical limitations, signal processing techniques, and machine learning options.

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