基于重分解和深度学习的系泊张力预测方法研究

范士杰; 张伟业; 缪维跑; 闻麒; 李春; 岳敏楠

doi:10.19805/j.cnki.jcspe.2025.240449

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基于重分解和深度学习的系泊张力预测方法研究

新能源与储能 | 更新时间：2026-10-10

- 基于重分解和深度学习的系泊张力预测方法研究
- Mooring-line Tension Prediction Based on Re-decomposition and Deep Learning
- 动力工程学报 2025年45卷第9期页码：1422-1432
- 作者机构：
  
  1. 上海理工大学能源与动力工程学院,上海,200093
  2. 上海非碳基能源转换与利用研究院,上海,200240
- 作者简介：
  
  范士杰(1998—),男,河南汝州人,硕士研究生,研究方向为风能利用,E-mail:sjfans0806@163.com
- 基金信息：
  
  国家自然科学基金资助项目(52106262,52376204);上海市IV类高峰学科-能源科学与技术-上海非碳基能源转换与利用研究院建设项目
- DOI：10.19805/j.cnki.jcspe.2025.240449
  中图分类号：
- 网络首发：2025-09-16，
  
  纸质出版：2025
- 稿件说明：
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范士杰,张伟业,缪维跑,闻麒,李春,岳敏楠. 基于重分解和深度学习的系泊张力预测方法研究动力工程学报, 2025, 45(9): 1422-1432 https://doi.

org/10.19805/j.cnki.jcspe.2025.240449
范士杰,张伟业,缪维跑,闻麒,李春,岳敏楠. 基于重分解和深度学习的系泊张力预测方法研究动力工程学报, 2025, 45(9): 1422-1432 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240449.

org/10.19805/j.cnki.jcspe.2025.240449 DOI：

摘要

海上漂浮式风力机平台依靠系泊系统的回复力减小平台运动并进行定位

而系泊的结构完整性可通过其力学特性反映

故准确预测系泊系统的张力有助于建立高效、健康的监测系统。基于深度学习方法对系泊张力进行健康监测

为解决数据驱动方法在预测时间序列时丢失物理信息的问题

利用自适应白噪声平均总体经验模态分解(CEEMDAN)与样本熵进行一次模态分解重构

并利用变分模态分解(VMD)与盒维数进行二次分解重构

构建出有效的特征样本集。通过引入注意力机制

并将其与卷积神经网络(CNN)和双向长短期记忆网络(BiLSTM)相结合

构建深度学习预测模型AM-CNN-BiLSTM。结果表明:该模型应用于有效特征样本集时

重分解重构方法能有效提取特征

提升模型预测精度;在各种海况和系泊系统设计方案下

所提模型均能实现精确预测

且预测结果的决定系数

均不小于0.879

充分证明该方法具有良好的泛化能力。

Abstract

Offshore floating wind turbine platforms rely on the restoring forces provided by the mooring system to reduce the platform motion and maintain position. As the structural integrity of the mooring can be reflected by its mechanical properties

accurate prediction of the mooring system tension is essential for an efficient and reliable health monitoring system. A data-driven approach based on deep learning was developed to predict mooring-line tension. To preserve physical information that was often lost in pure time-series forecasting

complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with sample entropy was applied for primary mode decomposition and reconstruc

tion. Variational mode decomposition (VMD) together with box dimension was employed for secondary decomposition and reconstruction

yielding a high-quality feature set. Attention mechanism was then integrated with a convolutional neural network and a bidirectional long short-term memory network (AM-CNN-BiLSTM) to construct the deep-learning tension-prediction model. Results show that the decomposition-reconstruction strategy effectively extracts informative features and improves prediction accuracy when applied to the feature set. Across diverse sea states and mooring-system configurations

the proposed model achieves precise tension predictions

with the coefficient-of-determination (

) values exceeding 0.879

confirming its strong generalization capability.

关键词

Keywords

references

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