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Energies 2019, 12(6), 1026; https://doi.org/10.3390/en12061026

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

1
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
2
China General Certification Center, Beijing 100020, China
3
INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal
*
Authors to whom correspondence should be addressed.
Received: 11 January 2019 / Revised: 3 March 2019 / Accepted: 4 March 2019 / Published: 15 March 2019
PDF [831 KB, uploaded 15 March 2019]

Abstract

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.
Keywords: wind turbine blade; full-scale static test; neural networks; strain prediction wind turbine blade; full-scale static test; neural networks; strain prediction
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, Z.; Liu, X.; Wang, K.; Liang, Z.; Correia, J.A.; De Jesus, A.M. GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades. Energies 2019, 12, 1026.

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