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

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

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
- Vol. 47, Issue 1, Pages: 701-708(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1674
  CLC： TK83
- Online First：05 February 2026，
  
  Published：05 February 2026
- 稿件说明：
移动端阅览
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：

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.

关键词

Keywords

references

何鸿圣, 李春, 王博, 等. 2种海上风力机漂浮式风电场平台动态响应对比[J]. 太阳能学报, 2023, 44(4): 1-8.HE H S, LI C, WANG B, et al. Comparison of dynamic response of two floating wind farm platforms for offshore wind turbines[J]. Acta energiae solaris sinica, 2023, 44(4): 1-8.

王博, 李春, 丁勤卫, 等. 基于正交化的混合式平台漂浮式风电场平台动态响应研究[J]. 太阳能学报, 2022, 43(1): 80-88.WANG B, LI C, DING Q W, et al. Research on dynamic response of hybrid platform floating wind farm platform based on orthogonalization[J]. Acta energiae solaris sinica, 2022, 43(1): 80-88.

KHAN A, BIL C, MARION K E.Ship motion prediction for launch and recovery of air vehicles[C]//Proceedings of OCEANS 2005 MTS/IEEE. Washington, DC, USA, 2006: 2795-2801.

BREMER K S.Using neural networks to predict the response of a floating structure[D]. Trondheim, Norwegian University of Science and Technologt, 2018.

MAZAHERI S.The usage of artificial neural networks in hydrodynamic analysis of floating offshore platforms[J]. The international journal of maritime engineering, 2006, 3(4): 48-60.

SHENG W, TONY L.Artificial neural network application in short-term prediction in an oscillating water column[C]//ISOPE International Ocean and Polar Engineering Conference. Beijing, China, 2010.

牟哲岳, 孙勇, 王瑞良, 等. 基于实测数据和机器学习的风电机组载荷预测模型[J]. 太阳能学报, 2023, 44(10): 414-419.MOU Z Y, SUN Y, WANG R L, et al. Prediction model for wind turbine loads based on experimental data and machine learning[J]. Acta energiae solaris sinica, 2023, 44(10): 414-419.

庞军恒, 黄炜楠, 董胜. 基于多变量DSD-LSTM模型的有效波高预测[J]. 太阳能学报, 2024, 45(7): 121-127.

魏强强. 基于深度学习的半潜平台运动响应分析与预测[D]. 上海: 上海交通大学, 2021.

GUO X X, ZHANG X T, TIAN X L, et al.Predicting heave and surge motions of a semi-submersible with neural networks[J]. Applied ocean research, 2021, 112: 102708.

陈海, 李志刚, 冯加果. 基于深度置信神经网络的半潜式平台浮体运动模型和响应预测研究[J]. 船舶力学, 2021, 25(5): 586-597.CHEN H, LI Z G, FENG J G. Prediction of motion and its distribution laws of semi-subsea platform based on deep learning methods[J]. Journal of ship mechanics, 2021, 25(5): 586-597.

GUO X X, ZHANG X T, TIAN X L, et al.Probabilistic prediction of the heave motions of a semi-submersible by a deep learning model[J]. Ocean engineering, 2022, 247: 110578.

姚骥. 基于深度学习的海洋环境、浮体响应预测及水下结构异常诊断[D]. 大连: 大连理工大学, 2022.

DENG Y F, FENG W, XU S W, et al.A novel approach for motion predictions of a semi-submersible platform with neural network[J]. Journal of marine science and technology, 2021, 26(3): 883-895.

HOCHREITER S, SCHMIDHUBER J.Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.

DYER C, BALLESTEROS M, LING W, et al.Transition-based dependency parsing with stack long short-term memory[J]. Computer science, 2015, 37(2): 321-332.

YU W, KOLJA M, FRANK L.D4.2 public definition of the two LIFES50+10 MW floater concepts[R]. Stuttgart: University of Stuttgart, 2018.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

基于ICPO优化VMD耦合深度学习模型的中短期风电功率预测

基于SSA-VMD预处理的TCN-Informer短期风速多步预测混合模型

基于Transformer多尺度融合网络的暖通空调能耗预测模型

基于深度残差注意力网络的光伏组件分割

基于循环DBSMOTE和多头注意力机制的风力发电机故障预警体系

Related Author

黄伟

刘彬

李火坤

黄俊

黄梓阳

孔宪正

黄国勇

邓为权

Related Institution

School of New Energy, Shenyang Institute of Engineering

Liaoning Solar Energy Research and Application Co., Ltd.

School of Communication and Information Engineering, Xi’an University of Science and Technology

Xi’an Dishan Vision Technology Limited Company

College of Electrical Engineering, Shanghai University of Electric Power

AI问答

⁰