{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T01:40:53Z","timestamp":1768095653821,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T00:00:00Z","timestamp":1552608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/en12061026","type":"journal-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T04:06:55Z","timestamp":1552882015000},"page":"1026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades"],"prefix":"10.3390","volume":"12","author":[{"given":"Zheng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Kan","family":"Wang","sequence":"additional","affiliation":[{"name":"China General Certification Center, Beijing 100020, China"}]},{"given":"Zhongwei","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China"}]},{"given":"Jos\u00e9 A.F.O.","family":"Correia","sequence":"additional","affiliation":[{"name":"INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1059-715X","authenticated-orcid":false,"given":"Ab\u00edlio M.P.","family":"De Jesus","sequence":"additional","affiliation":[{"name":"INEGI, Faculty of Engineering, University of Porto, Porto 4200-465, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.rser.2011.07.154","article-title":"A review and design study of blade testing systems for utility-scale wind turbines","volume":"16","author":"Malhotra","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.compstruct.2016.12.037","article-title":"Physical experimental static testing and structural design optimization for a composite wind turbine blade","volume":"16","author":"Fagan","year":"2017","journal-title":"Compos. Struct."},{"key":"ref_3","first-page":"33","article-title":"Static load strain test of wind turbine blades","volume":"30","author":"Yang","year":"2011","journal-title":"Res. Explor. Lab."},{"key":"ref_4","first-page":"1491","article-title":"Effects of structure nonlinear on full-scale wind turbine blade static test","volume":"45","author":"Pan","year":"2017","journal-title":"J. Tongji Univ. (Nat. Sci.)"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhu, S.P., Yue, P., Yu, Z.Y., and Wang, Q.Y. (2017). A combined high and low cycle fatigue model for life prediction of turbine blades. Materials, 10.","DOI":"10.3390\/ma10070698"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.engfracmech.2018.08.009","article-title":"Computational framework for multiaxial fatigue life prediction of compressor discs considering notch effects","volume":"202","author":"Liao","year":"2018","journal-title":"Eng. Fract. Mech."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Meng, D., Yang, S., Zhang, Y., and Zhu, S.P. (2018). Structural reliability analysis and uncertainties-based collaborative design and optimization of turbine blades using surrogate model. Fatigue Fract. Eng. Mater. Struct.","DOI":"10.1111\/ffe.12906"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.ijmecsci.2018.04.050","article-title":"Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks","volume":"142\u2013143","author":"Zhu","year":"2018","journal-title":"Int. J. Mech. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1687814018783410","article-title":"A fluid-structure analysis approach and its application in the uncertainty-based multidisciplinary design and optimization for blades","volume":"10","author":"Meng","year":"2018","journal-title":"Adv. Mech. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tarfaoui, M., Nachtane, M., and Boudounit, H. (2018). Finite element analysis of composite offshore wind turbine blades under operating conditions. J. Therm. Sci. Eng. Appl.","DOI":"10.1115\/1.4042123"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"051204","DOI":"10.1115\/1.4042414","article-title":"Design and optimization of composite offshore wind turbine blades","volume":"141","author":"Tarfaoui","year":"2019","journal-title":"J. Energy Resour. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s10443-017-9612-x","article-title":"Simulation of mechanical behavior and damage of a large composite wind turbine blade under critical loads","volume":"25","author":"Tarfaoui","year":"2018","journal-title":"Appl. Compos. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s00158-016-1462-x","article-title":"Reliability-based design optimization of wind turbine blades for fatigue life under dynamic wind load uncertainty","volume":"54","author":"Hu","year":"2016","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s00158-015-1338-5","article-title":"Integrating variable wind load, aerodynamic, and structural analyses towards accurate fatigue life prediction in composite wind turbine blades","volume":"53","author":"Hu","year":"2016","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_15","first-page":"800","article-title":"Fingerprint Verification Based on Invariant Moment Features and Nonlinear BPNN","volume":"6","author":"Yang","year":"2008","journal-title":"Int. J. Control. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.measurement.2016.05.049","article-title":"Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN\u2013PSO approach","volume":"92","author":"Moghaddam","year":"2016","journal-title":"Measurement"},{"key":"ref_17","unstructured":"Huang, Q., Jiang, D., Hong, L., and Ding, Y. (2008). Application of Wavelet Neural Networks on Vibration Fault Diagnosis for Wind Turbine Gearbox, Springer."},{"key":"ref_18","first-page":"14","article-title":"A generalized model for wind turbine faulty condition detection using combination prediction approach and information entropy","volume":"32","author":"Chen","year":"2018","journal-title":"J. Environ. Inf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1016\/j.jclepro.2018.05.126","article-title":"An anomaly identification model for wind turbine state parameters","volume":"195","author":"Zhang","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5114","DOI":"10.21595\/jve.2017.18240","article-title":"The limit cycle oscillation of divergent instability control based on classical flutter of blade section","volume":"19","author":"Liu","year":"2017","journal-title":"J. Vibro Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s10462-011-9208-z","article-title":"An optimizing BP neural network algorithm based on genetic algorithm","volume":"36","author":"Ding","year":"2011","journal-title":"Artif. Intell. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"18749","DOI":"10.1007\/s11042-016-4319-9","article-title":"A novel macroeconomic forecasting model based on revised multimedia assisted BP neural network model and ant Colony algorithm","volume":"76","author":"Kuang","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1080\/15435075.2017.1333433","article-title":"Optimization of thermal performance of the parabolic trough solar collector systems based on GA-BP neural network model","volume":"14","author":"Wang","year":"2017","journal-title":"Int. J. Green Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1007\/s12206-018-0223-8","article-title":"Multidisciplinary robust design optimization based on time-varying sensitivity analysis","volume":"32","author":"Xu","year":"2018","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_25","unstructured":"Zhou, W.H., and Xiong, S.Q. (2013). Optimization of BP Neural Network Classifier Using Genetic Algorithm, Springer."},{"key":"ref_26","unstructured":"GB\/T 25384-2010 (2010). Turbine Blade of Wind Turbine Generator Systems-Full-Scale Structural Test of Rotor Blades, Standards Press of China."}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/12\/6\/1026\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:39:15Z","timestamp":1760186355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/12\/6\/1026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,15]]},"references-count":26,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["en12061026"],"URL":"https:\/\/doi.org\/10.3390\/en12061026","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,15]]}}}