MOTION PREDICTIONS OF 10 MW FLOATING OFFSHORE WIND TURBINE PLATFORM BASED ON BI-LSTM

Zhang Xianfeng; Yin Jiaqing; Ma Lu; Qin Ming; Lei Xiao; Yang Yang

doi:10.19912/j.0254-0096.tynxb.2024-1674

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 701-708. DOI: 10.19912/j.0254-0096.tynxb.2024-1674

MOTION PREDICTIONS OF 10 MW FLOATING OFFSHORE WIND TURBINE PLATFORM BASED ON BI-LSTM

Zhang Xianfeng¹, Yin Jiaqing², Ma Lu¹, Qin Ming¹, Lei Xiao¹, Yang Yang²

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Abstract

This study has developed a method for the motion prediction of a 10 MW floating wind platform under the action of waves, based on the Bi-directional long-short-term memory（Bi-LSTM） neural network. By simulating a 10 MW floating offshore wind power platform, wave and motion time series are obtained for a parameter sensitivity analysis. The simulation data are used to train the Bi-LSTM neural network framework and the parameters are then optimized. The results show that the developed Bi-LSTM model is highly effective in predicting the motion of the floating offshore wind platform under wave action in the next 1/3 time-length of the input data considering different wave heights and spectral peak frequencies. The prediction accuracy of the wave-induced heave and surge is as high as 95%. Therefore, the method proposed in this study has a strong ability to predict platform motion and is of great importance for the development of offshore wind energy.

Key words

floating wind power platform / deep learning / bi-directional long short-term memory（Bi-LSTM） / motion prediction / neural network / wave load

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Zhang Xianfeng, Yin Jiaqing, Ma Lu, Qin Ming, Lei Xiao, Yang Yang. MOTION PREDICTIONS OF 10 MW FLOATING OFFSHORE WIND TURBINE PLATFORM BASED ON BI-LSTM[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 701-708 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1674

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