Quantification Method for Ice Accretion on Wind Turbine Blades Using Variational Network

WANG Qianyue; SI Gangquan; YIN Junhua; TONG Wenhan; LI Bo; WANG Xin

doi:10.7652/xjtuxb202508019

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Quantification Method for Ice Accretion on Wind Turbine Blades Using Variational Network

更新时间：2025-07-30

- Quantification Method for Ice Accretion on Wind Turbine Blades Using Variational Network
- Journal of Xi’an Jiaotong University Vol. 59, Issue 8, Pages: 199-210(2025)
- 作者机构：
  
  1.西安交通大学电工材料电气绝缘全国重点实验室， 710049，西安
  2.西安交通大学电气工程学院， 710049，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202508019
  CLC： TP29
- Received：30 December 2024，
  
  Online First：10 April 2025，
  
  Published：10 August 2025
- 稿件说明：
移动端阅览
WANG Qianyue, SI Gangquan, YIN Junhua, et al. Quantification Method for Ice Accretion on Wind Turbine Blades Using Variational Network[J]. Journal of Xi’an Jiaotong University, 2025, 59(8): 199-210.
DOI：

WANG Qianyue, SI Gangquan, YIN Junhua, et al. Quantification Method for Ice Accretion on Wind Turbine Blades Using Variational Network[J]. Journal of Xi’an Jiaotong University, 2025, 59(8): 199-210. DOI： 10.7652/xjtuxb202508019.

摘要

针对现有变分网络在风力发电机组监测数据特征提取和故障敏感性方面的不足，提出了一种面向风机正常状态建模的条件变分递归窗口网络（CVRWN），并据此构建叶片结冰程度量化方法。首先，采用引入自注意力机制的多组条件变分自编码器（CVAE）对窗口化的风机监测数据进行学习，以实现各时间窗口的潜在分布特征提取与风功率数据重构；其次，通过门控循环单元（GRU）实现跨窗口特征传递，并引入预测子模块以增强长期趋势建模能力，从而构建完整的CVRWN网络结构；随后，利用风机正常运行数据对所构建网络进行训练，通过联合优化重构损失与预测损失得到具备稳定建模能力的CVRWN模型；最终，将CVRWN模型最后一个窗口的风功率重构损失定义为风机叶片结冰指数

，实现叶片结冰程度的精准量化。实际运行数据验证表明，采用CVRWN模型进行风功率重构时，其精度较基础CVAE模型提升约10%，验证了所提模型结构改进的合理性与有效性。在叶片结冰过程中，相较于基线模型，所提模型的结冰指数

能够精准动态表征叶片结冰演化过程，为极端环境下风力发电机组的安全运维提供量化参考。

Abstract

To address the limitation of existing variational networks in feature extraction and fault sensitivity for wind turbine monitoring data

this paper proposes a conditional variational recurrent window network (CVRWN) aimed at modeling the normal operating state of wind turbines

and constructs a quantification method for ice accretion on blades based on such network. First

multiple groups of conditional variational autoencoders (CVAEs) enhanced with self-attention mechanisms are employed to learn from windowed monitoring data

enabling the extraction of latent distribution features and the reconstruction of wind power data for each time window. Next

gated recurrent units (GRUs) are used to facilitate cross-window feature transfer

and a predictive submodule is introduced to enhance long-term trend modeling capability

thereby constructing the complete CVRWN architecture. Subsequently

the constructed network is trained on turbine monitoring data under normal operating conditions

resulting in a stable CVRWN model obtained by jointly optimizing reconstruction and prediction losses. Finally

the reconstruction loss of wind power in the last window of the CVRWN model is defined as the ice accretion indicator

which enables accurate quantification of the icing levels. Validation with actual operational data demonstrates that the CVRWN model improves wind power reconstruction accuracy by approximately 10% compared to the basic CVAE model

verifying the rationality and effectiveness of the proposed structural enhancements. During the ice accretion process

the proposed model's ice accretion indicator

can accurately and dynamically represent the evolution of blade icing compared to baseline models

providing a quantitative reference for the sa

fe operation and maintenance of wind turbines in extreme environments.

关键词

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references

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