Open Access

ARTICLE

Data-Driven Modeling for Wind Turbine Blade Loads Based on Deep Neural Network

Jianyong Ao¹, Yanping Li¹, Shengqing Hu¹, Songyu Gao², Qi Yao^2,*
1 Yangjiang Power Supply Bureau, Guangdong Power Grid, Yangjiang, 529500, China
2 Energy and Electricity Research Center, Jinan University, Zhuhai, 519070, China
* Corresponding Author: Qi Yao. Email:
(This article belongs to the Special Issue: AI-application in Wind Energy Development and Utilization)

Energy Engineering https://doi.org/10.32604/ee.2024.055250

Received 21 June 2024; Accepted 21 August 2024; Published online 03 September 2024

Abstract

Blades are essential components of wind turbines. Reducing their fatigue loads during operation helps to extend their lifespan, but it is difficult to quickly and accurately calculate the fatigue loads of blades. To solve this problem, this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis, feature selection, and model construction. In the mechanism analysis part, the generation mechanism of blade loads and the load theoretical calculation method based on material damage theory are analyzed, and four measurable operating state parameters related to blade loads are screened; in the feature extraction part, 15 characteristic indicators of each screened parameter are extracted in the time and frequency domain, and feature selection is completed through correlation analysis with blade loads to determine the input parameters of data-driven modeling; in the model construction part, a deep neural network based on feedforward and feedback propagation is designed to construct the nonlinear coupling relationship between the unit operating parameter characteristics and blade loads. The results show that the proposed method mines the wind turbine operating state characteristics highly correlated with the blade load, such as the standard deviation of wind speed. The model built using these characteristics has reasonable calculation and fitting capabilities for the blade load and shows a better fitting level for untrained out-of-sample data than the traditional scheme. Based on the mean absolute percentage error calculation, the modeling accuracy of the two blade loads can reach more than 90% and 80%, respectively, providing a good foundation for the subsequent optimization control to suppress the blade load.

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Keywords

Wind turbine; blade; fatigue load modeling; deep neural network

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