基于BIRCH-MSSCN模型的风电叶片结冰故障预警

李练兵; 雒威; 程晴; 苏文勇; 陈业策; 卢志辉

doi:10.19912/j.0254-0096.tynxb.2025-0010

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基于BIRCH-MSSCN模型的风电叶片结冰故障预警

更新时间：2026-07-21

- 基于BIRCH-MSSCN模型的风电叶片结冰故障预警
- Vol. 47, Issue 5, Pages: 341-349(2026)
- 作者机构：
- 作者简介：
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- DOI：10.19912/j.0254-0096.tynxb.2025-0010
  CLC： TM315
- Online First：04 June 2026，
  
  Published：2026
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李练兵, 雒威, 程晴, et al. 基于BIRCH-MSSCN模型的风电叶片结冰故障预警[J]. 2026, 47(5): 341-349.
DOI：

李练兵, 雒威, 程晴, et al. 基于BIRCH-MSSCN模型的风电叶片结冰故障预警[J]. 2026, 47(5): 341-349. DOI： 10.19912/j.0254-0096.tynxb.2025-0010.

摘要

为提前预警风电叶片的结冰故障

提出一种基于平衡迭代缩减聚类（BIRCH）、梅尔频谱图、暹罗网络和卷积基础的多尺度注意力机制（CBAM）的BIRCH-MSSCN模型。该模型通过对风电叶片结冰前状态的标记、正常数据的下采样、暹罗网络的相似度度量以及CBAM注意力机制对特征的选择

实现结冰故障的提前预警。对比分析结果表明

在保证较低计算量的同时

该模型能够早在42 min前实现较为准确的结冰故障预警。该模型为风电机组的高效、稳定运行提供了更为可靠的预警信息

具有重要的应用价值。

Abstract

To provide early warnings for blade icing faults

a BIRCH-MSSCN model is proposed

based on Balanced Iterative Reducing and Clustering using Hierarchies （BIRCH）

Mel spectrograms

Siamese networks

and the Convolutional-based Multi-scale Attention Mechanism （CBAM）. The model achieves early fault warnings for blade icing through labeling pre-icing states

down-sampling normal data

measuring similarity using the Siamese network

and selecting features with the CBAM attention mechanism. Comparative analysis results indicate that while maintaining low computational costs

the model can accurately predict icing faults up to 42 minutes in advance. This model provides more reliable early warning information for the efficient and stable operation of wind turbines

showcasing significant application value.

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