Acoustic Emission Monitoring of Small Wind Turbine Blades

Author(s):  
P. A. Joosse ◽  
M. J. Blanch ◽  
A. G. Dutton ◽  
D. A. Kouroussis ◽  
T. P. Philippidis ◽  
...  

Wind turbine blade certification tests, comprising a static test, a fatigue test, and finally a residual strength test, often involve sudden audible cracking sounds from somewhere within the blade, without the operators being able to locate the noise source, or to determine whether damage (minor or major) has occurred. A current EC-funded research project is looking at the possibility of using acoustic emission (AE) monitoring during testing of fibre composite blades to detect such events and assess the blade condition. AE can both locate and characterise damage processes in blades, starting with non-audible signals occurring due to damage propagation at relatively low loads. The test methodology is discussed in the context of the blade certification procedure and results are presented from a series of static and fatigue blade tests to failure in the laboratory. Inferences are drawn about small differences in the manufacture of the nominally identical blades and conclusions are presented for the application of the methodology.

2002 ◽  
Vol 124 (4) ◽  
pp. 446-454 ◽  
Author(s):  
P. A. Joosse ◽  
M. J. Blanch ◽  
A. G. Dutton ◽  
D. A. Kouroussis ◽  
T. P. Philippidis ◽  
...  

Wind turbine blade certification tests often generate sudden audible cracking sounds from somewhere within the blade, without the operators being able to locate the noise source or to evaluate the existence or the extent of any damage. It would be beneficial to be able to detect any damage incurred by the blade, whether it is accompanied by audible noise or not. The current project, named AEGIS, is looking at the possibility of using acoustic emission monitoring during testing of fiber composite blades to detect the source of damage events and assess the blade condition. The test methodology is discussed in the context of the blade certification procedure and results are presented from a series of static and fatigue blade tests to failure in the laboratory.


2011 ◽  
Author(s):  
Zhichun Zhang ◽  
Zhong Huang ◽  
Yanjiu Liu ◽  
Jinsong Leng

Author(s):  
Nikolaos K. Tsopelas ◽  
Dimitrios G. Papasalouros ◽  
Athanasios A. Anastasopoulos ◽  
Dimitrios A. Kourousis ◽  
Jason W. Dong

2015 ◽  
Vol 35 (3) ◽  
pp. 179-184
Author(s):  
Hyun-Sup Jee ◽  
No-Hoe Ju ◽  
Cheal Ho So ◽  
Jong-Kyu Lee

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1026 ◽  
Author(s):  
Zheng Liu ◽  
Xin Liu ◽  
Kan Wang ◽  
Zhongwei Liang ◽  
José A.F.O. Correia ◽  
...  

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.


Author(s):  
N Tsopelas ◽  
D Kourousis ◽  
I Ladis ◽  
A Anastasopoulos ◽  
D Lekou ◽  
...  

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