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

Zheng Liu, Xin Liu, Kan Wang, Zhongwei Liang, José A.F.O. Correia and Abílio M.P. De Jesus
Additional contact information
Zheng Liu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Xin Liu: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
Kan Wang: China General Certification Center, Beijing 100020, China
Zhongwei Liang: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
José A.F.O. Correia: INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal
Abílio M.P. De Jesus: INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal

Energies, 2019, vol. 12, issue 6, 1-15

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/6/1026/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/6/1026/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:6:p:1026-:d:214402

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().