A prediction method for blade deformations of large-scale FVAWTs using dynamics theory and machine learning techniques

Tools

Deng, Wanru and Liu, Liqin and Dai, Yuanjun and Wu, Haitao and Yuan, Zhiming (2024) A prediction method for blade deformations of large-scale FVAWTs using dynamics theory and machine learning techniques. Energy, 304. 132211. ISSN 1873-6785 (https://doi.org/10.1016/j.energy.2024.132211)

[thumbnail of Deng-etal-2024-A-prediction-method-for-blade-deformations-of-large-scale-FVAWTs]
Preview
 Text. Filename: Deng-etal-2024-A-prediction-method-for-blade-deformations-of-large-scale-FVAWTs.pdf
Accepted Author Manuscript
License: Creative Commons Attribution 4.0 logo
Download (4MB)| Preview

Abstract

There is renewed interest in floating vertical axis wind turbines (FVAWTs) as offshore wind turbines progressively increase in size and move into deeper waters. To explore the potential of large-scale FVAWTs for future commercialization, it is crucial to investigate blade deformations using an accurate and effective method. In this study, we developed a hybrid model, namely, the SVST-ANN, which integrates dynamic theory and machine learning techniques to predict blade deformations. Specifically, an artificial neural network (ANN) module is incorporated into the slack coupled vertical axis wind turbine simulation tool (SVST), which significantly reduces the total computational time. A comparative study was conducted between the SVST-ANN model and the traditional SVST model, employing a 10 MW helical-type FVAWT as an example. The results show that the SVST-ANN model can accurately and efficiently predict blade deformations. The maximum errors for the maximum value, average value, and standard deviation across all nodes are minimal, with a corresponding computational time reduction of approximately 60 %. This study provides a novel method for investigating the dynamic behavior of the FVAWTs, which is more effective for calculating the elastic deformations of blades than traditional numerical methods.

ORCID iDs

Deng, Wanru, Liu, Liqin, Dai, Yuanjun, Wu, Haitao and Yuan, Zhiming ORCID logoORCID: https://orcid.org/0000-0001-9908-1813;
CORE (COnnecting REpositories)