Physics-informed deep learning model in wind turbine response prediction

Xuan Li and Wei Zhang

Renewable Energy, 2022, vol. 185, issue C, 932-944

Abstract: Subjected to strong cyclic wind and wave loads, wind turbines could experience severe fatigue damages and possibly fail to function normally due to accumulated damages at certain critical locations. Therefore, fatigue damage evaluation and prediction are essential and important to be conducted, which could involve massive numerical simulations and computational costs due to dynamic analyses of the wind turbines under various environmental conditions. To reduce the calculation cost related to the time-consuming dynamic analysis, sequence models such as the recurrent neural network (RNN) and the long-short term memory model (LSTM) originated from the deep learning topic are good and promising candidates to predict structural dynamic responses at multiple critical locations under different environmental scenarios. However, the training cost and prediction accuracy of these deep learning models might not be satisfiable since these models are purely data-driven and require significant amount of training data and a large number of training parameters. To reduce the computational cost and improve the prediction accuracy, a hybrid method that integrates the physical information of the underlying wind turbine system into the data-driven model is implemented in the present study as a computationally efficient simulation model. Structural properties and linearized representations of the wind turbine system are served as the physical constraints and applied in a recently proposed deep residual recurrent neural network (DR-RNN) to form as a physics-informed deep learning model. This physics-informed model is first applied to a frame structure with four degrees of freedom as a benchmark study to show the accuracy and efficiency of this model. The applicability of this physics-informed model to a complex wind turbine system is then investigated, and the performance of the developed physics-informed model on the structural response prediction is also compared with a regular data-driven model.

Keywords: Physics-informed deep learning model; Wind turbine; Recurrent neural network (RNN); Long-short term memory (LSTM); Structure linearization; Response prediction (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148121017791
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:185:y:2022:i:c:p:932-944

DOI: 10.1016/j.renene.2021.12.058

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().