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

Clicks: 264
ID: 111461
2019
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #483 of 812 articles by views in Materials (Basel, Switzerland)

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 812 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
liu2019materialspso-bp Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xin Liu;Zheng Liu;Zhongwei Liang;Shun-Peng Zhu;José A. F. O. Correia;Abílio M. P. De Jesus;Liu, Xin;Liu, Zheng;Liang, Zhongwei;Zhu, Shun-Peng;Correia, José A. F. O.;De Jesus, Abílio M. P.;
Journal Materials (Basel, Switzerland)
Year 2019
DOI
10.3390/ma12121889
URL
https://www.mdpi.com/1996-1944/12/12/1889
Keywords
wind turbine blade full-scale static test pso-bp neural network strain prediction National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine pmid:31212753 pmc6632004 doi:10.3390/ma12121889 xin liu zheng liu abílio m p de jesus

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.