基于Bi-LSTM的10 MW漂浮式风电平台运动预测

张险峰; 尹佳晴; 马璐; 秦明; 雷肖; 杨阳

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

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基于Bi-LSTM的10 MW漂浮式风电平台运动预测

更新时间：2026-02-06

- 基于Bi-LSTM的10 MW漂浮式风电平台运动预测
- 基于Bi-LSTM的10 MW漂浮式风电平台运动预测
- 太阳能学报 2026年47卷第1期页码：701-708
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家重点研发计划（2023YFE0102000）;中国长江三峡集团有限公司科研项目（202303059）;国家自然科学基金（52476205）;高等学校学科创新引智计划（111计划）“跨海大桥安全保障与智能运行学科创新引智基地(D21013)”
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1674
  中图分类号： TK83
- 网络首发：2026-02-05，
  
  纸质出版：2026-02-05
- 稿件说明：
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张险峰, 尹佳晴, 马璐, 等. 基于Bi-LSTM的10 MW漂浮式风电平台运动预测[J]. 太阳能学报, 2026,47(1):701-708.

张险峰, 尹佳晴, 马璐, et al. 基于Bi-LSTM的10 MW漂浮式风电平台运动预测[J]. 2026, 47(1): 701-708.
张险峰, 尹佳晴, 马璐, 等. 基于Bi-LSTM的10 MW漂浮式风电平台运动预测[J]. 太阳能学报, 2026,47(1):701-708. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1674.

张险峰, 尹佳晴, 马璐, et al. 基于Bi-LSTM的10 MW漂浮式风电平台运动预测[J]. 2026, 47(1): 701-708. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1674.

摘要

基于双向长短期记忆神经网络（Bi-LSTM）建立针对于10 MW漂浮式海上风电平台在波浪作用下的平台运动响应预测模型

通过仿真计算得到大量波浪时间序列信息以及运动响应数据

针对这些数据进行参数敏感性分析

训练后优化参数以确定最优的Bi-LSTM神经网络结构。结果表明

通过考虑不同波高和谱峰频率的波浪条件

验证了Bi-LSTM神经网络方法的可行性。所建立的Bi-LSTM模型对预测输入数据1/3时长的漂浮式海上风电平台在波浪作用下的运动具有较高的准确率

纵荡、垂荡和纵摇的预报精度高达95%

因此所提方法具有较强的平台运动预测能力。

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.

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Keywords

references

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