GWO-LSSVM-based Strain Prediction and Importance Analysis for Full-scale Static Testing of Composite Wind Turbine Blades

Abstract

The strain measuring data in wind turbine blade (WTB) full-scale static testing is the basic for WTB mechanical property analysis, reliability assessment, design optimization, etc.; thus, accurate and sufficient strain data is essential. However, in the WTB full-scale static performance testing, the numbers of strain measuring positions are limited, thus the strain data for further analysis is insufficient. Since the strain response of the blade has a strong correlation with the applied load, measured positions, and is also directly influenced by the geometric non-linear of the blade structure, traditional numerical analysis methods based on physical simulation and mathematical models are difficult to meet the needs of accurate strain acquisition. Considering the significant advantages of Grey Wolf Optimizer-Least Squares Support Vector Machine (GWO-LSSVM) in dealing with multi-input parameters, non-linear fitting, etc., In this paper, a strain prediction method based on GWO-LSSVM for full-scale static testing of WTB is proposed in combination with full-scale static test data of a certain type of WTB. The accuracy and effectiveness of the proposed method are compared with traditional Least Squares Support Vector Machine (LSSVM) and Back Propagation Neural Network (BPNN). The study can provide more information for WTB reliability assessment and lifetime prediction.