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Short-term motion prediction of floating offshore wind turbine based on muti-input LSTM neural network by Wei Shi; Lehan Hu; Zaibin Lin; Lixian Zhang; Jun Wu; Wei Chai is a Engineering article available to read on EtoBox.

What is Short-term motion prediction of floating offshore wind turbine based on muti-input LSTM neural network about?

The motion response of an offshore floating wind turbine (FOWT) platform is closely related to the control operation regarding the safety of a wind turbine. It is affected by various factors such as sea state environments and mooring systems. In practice, how to predict the motion response of the wind turbine platform in the short term has always been a concern of engineering practice. At present, the development of deep learning technology has brought some potential solutions to this problem. In this paper, a Multi-Input Long-Short Term Memory (MI-LSTM) neural network method is proposed to predict the short-term motion response of a floating offshore wind turbine platform. Specifically, the numerical simulation of the 5 MW Braceless platform is carried out under different environmental conditions, and the data of platform motion response, wave elevation, and mooring force are selected as input variables. Then the training and test groups are established after post-processing data. Subsequently, a Single-Input LSTM (SI-LSTM) model and a Multi-Input LSTM (MI-LSTM) model are established to learn the input data. After comparing the overall accuracy of the results, it is found that the

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Author

Wei Shi; Lehan Hu; Zaibin Lin; Lixian Zhang; Jun Wu; Wei Chai

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)