Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (9): 2489-2501.doi: 10.13229/j.cnki.jdxbgxb.20250245

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Blade ice detection algorithm based on dual channel temporal feature fusion network

Qiu-zhan ZHOU1(),Xiao CHEN1,Feng WANG2,Wen ZHANG2,Chen LI2,Hui-nan WU1(),Wan-chao DENG1,Ping-ping LIU3   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130012，China
    2.Sinovel Wind Power Technology（jiangsu） Co. ，Ltd. ，Yancheng 224056，China
    3.College of Computer Science and Technology，Jilin University，Changchun 130012，China
  • Received:2025-03-24 Online:2026-09-01 Published:2026-09-07
  • Contact: Hui-nan WU E-mail:13504465154@163.com;466534739@qq.com

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Abstract:

As a clean and renewable energy source， wind energy has been extensively promoted globally. However， the issue of wind turbine blade icing has severely impacted the safe operation and efficiency of the equipment. Existing blade icing detection algorithms are prone to being affected by data imbalance or noise， leading to reduced accuracy. Consequently， there is an urgent need for a precise and efficient ice detection algorithm to mitigate operational risks. In this paper， we propose an ice detection algorithm for wind turbine blades based on a dual-channel temporal feature fusion network. First， we preprocess Supervisory Control and Data Acquisition （SCADA） data and apply feature engineering to SCADA data. Secondly， a detection model combining Long Short-Term Memory Network （LSTM） and attention mechanism is proposed. LSTM is used to fully learn the time dependence of fan state， and the attention mechanism is used to dynamically adjust the weights of different time steps. Finally， a blade ice detection algorithm based on dual channel temporal feature fusion network is proposed. Dynamic trends of features over time can be captured. Multi-head attention mechanism is introduced to enhance feature interaction. The experimental results show that： compared with other algorithms， the detection accuracy of the proposed algorithm is improved by 10%-15%. It has good performance in the ice detection of fan blades.

Key words: Icing detection, Temporal feature fusion, Long short-term memory network, Attention mechanism

CLC Number: 

  • TP274

Fig. 1

LSTM-Attention ice detection model"

Fig. 2

LSTM-Attention model training process"

Fig. 3

Dual channel temporal feature fusion network detection model"

Table 1

Model hyperparameter Settings"

参数值
Batch size12
学习率0.001
Epoch100
Dropout0.2
优化器Adam

Fig. 4

Collinear feature analysis"

Table 2

Fan and environment feature selection"

环境特征权重风机特征权重
环境温度0.163发电机转速0.160
风俗0.160有功功率0.156
风向角0.156变桨电机温度10.136
平均风向角0.136X方向加速度0.070

Fig. 5

Wind speed - power scatter plot"

Fig. 6

Wind speed - power fitting curve"

Fig. 7

Wind speed - generator speed scatter plot"

Fig. 8

Wind speed - generator speed fitting curve"

Fig. 9

LSTM-Attention loss changes in training sets and validation sets"

Fig. 10

Effect of DTFF-Net feature number on model accuracy"

Fig. 11

DTFF-Net loss changes in training sets and validation sets"

Fig. 12

Comparison of algorithm accuracy"

Table 3

Comparative experimental results"

模型名称准确率精确率召回率F1值
XGBoost0.7490.7130.7020.707
LSTM0.7510.7380.7480.743
SVM0.7580.7640.7840.774
LSTM+Attention0.8530.8190.8290.824
DTFF-Net0.8940.8670.8640.866

Fig. 13

Comparison of ROC curves of different algorithms"

Table 4

Models for ablation experiments"

模型编号模型名称修改内容说明
D1完整DTFF-Net保留双通道、残差特征、多头注意力模块
D2w/o Dual-Channel将风机与环境特征合并输入单通道LSTM，移除通道区分建模结构
D3w/o Attention移除多头注意力模块，改为简单连接融合两通道输出特征
D4w/o Residual Input不使用风速-功率残差和风速-转速残差，直接使用原始功率和转速特征

Table 5

Results of ablation experiments"

模型编号准确率精确率召回率F1值
D189.486.786.486.6
D285.582.381.681.9
D384.881.980.781.3
D482.980.278.579.3
[1] 柯坚. 碳达峰碳中和战略的法治回应[J]. 河北大学学报: 哲学社会科学版, 2025, 50(1): 123-124.
Ke Jian. The rule of law response to the Carbon Peak Carbon neutral strategy[J]. Journal of Hebei University (Philosophy and Social Science),2025, 50(1):123-124.
[2] 安祥飞, 楚延锋. 风力发电机叶片防覆冰涂层研究进展[J]. 河南科技, 2024, 51(15): 76-80.
An Xiang-fei, Chu Yan-feng. Research progress in anti-icing coating for wind turbine blades[J]. Henan Science and Technology, 2024, 51(15): 76-80.
[3] 余思锐, 宋孟杰, 沈俊, 等. 低温静止与运动表面结冰特性预测技术研究进展[J]. 哈尔滨工业大学学报, 2024, 56(10): 150-168.
Yu Si-rui, Song Meng-jie, Shen Jun, et al. Research progress on icing characteristic prediction technologies for low temperature stationary and moving surfaces[J]. Journal of Harbin Institute of Technology, 2024, 56(10): 150-168.
[4] Pérez J M P, Márquez F P G, Hernández D R. Economic viability analysis for icing blades detection in wind turbines[J]. Journal of Cleaner Production, 2016, 135: 1150-1160.
[5] Jiang G Q, Yue R X, He Q, et al. Imbalanced learning for wind turbine blade icing detection via spatio-temporal attention model with a self-adaptive weight loss function[J]. Expert Systems with Applications, 2023, 229: 120428.
[6] Guo J C, Song X W, Tang S F, et al. Fault diagnosis of wind turbine blade icing based on feature engineering and the PSO-ConvLSTM-transformer[J]. Ocean Engineering, 2024, 302: 117726.
[7] Tian W W, Cheng X, Li G Y, et al. A multilevel convolutional recurrent neural network for blade icing detection of wind turbine[J]. IEEE Sensors Journal, 2021, 21(18): 20311-20323.
[8] Chen W H, Cheng L S, Chang Z P, et al. Wind turbine blade icing detection using a novel bidirectional gated recurrent unit with temporal pattern attention and improved coot optimization algorithm[J]. Measurement Science and Technology, 2023, 34(1): 014004.
[9] Jiang G Q, Li W Y, Bai J R, et al. SCADA data-driven blade icing detection for wind turbines: an enhanced spatio-temporal feature learning approach[J]. Measurement Science and Technology, 2023, 34(5): 054004.
[10] Wang L, He Y G, Zhou Y Z, et al. A novel approach to wind turbine blade icing detection with limited sensor data via spatiotemporal attention Siamese network[J]. IEEE Transactions on Industrial Informatics, 2024,20(6): 8993-9005.
[11] 张好雨. 基于SCADA数据的风机叶片覆冰故障检测与预测[D]. 北京: 北京交通大学 机械与电子控制工程学院, 2022.
Zhang Hao-yu. Detection and prediction of wind turbine blade icing fault based on SCADA data[D]. Beijing: College of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, 2022.
[12] 单锐, 杨婧, 朱文元, 等. 不同缺失比例下的缺失值插补方法比较[J]. 信息技术, 2023 (12): 52-56.
Shan Rui, Yang Jing, Zhu Wen-yuan, et al. Comparison of missing value interpolation methods under different missing ratios[J]. Information Technology, 2023 (12): 52-56.
[13] Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321-357.
[14] Puth M T, Neuhäuser M, Ruxton G D. Effective use of Pearson's product-moment correlation coefficient[J]. Animal Behaviour, 2014, 93: 183-189.
[15] Ke G L, Meng Q, Finley T, et al. Lightgbm: a highly efficient gradient boosting decision tree[C]∥ NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, New Orleans, USA, 2017.
[16] Parent O, Ilinca A. Anti-icing and de-icing techniques for wind turbines: Critical review[J]. Cold Regions Science and Technology, 2011, 65(1): 88-96.
[17] Skrimpas G A, Kleani K, Mijatovic N, et al. Detection of icing on wind turbine blades by means of vibration and power curve analysis[J]. Wind Energy, 2016, 19(10): 1819-1832.
[18] 胡梦婷, 罗晨. 基于MCNN-LSTM和交叉熵损失函数的轴承故障诊断[J]. 制造技术与机床, 2024, (9): 16-22.
Hu Meng-ting, Luo Chen. Bearing fault diagnosis based on MCNN-LSTM and cross entropy loss function[J]. Manufacturing Technology & Machine Tool,2024 (9): 16-22.
[19] Zhang Y, Xing K S, Bai R X, et al. An enhanced convolutional neural network for bearing fault diagnosis based on time-frequency image[J]. Measurement, 2020, 157: 107667.
[20] Chandra M A, Bedi S S. Survey on SVM and their application in image classification[J]. International Journal of Information Technology, 2021, 13(5): 1-11.
[21] Choe D E, Kim H C, Kim M H. Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades[J]. Renewable Energy, 2021, 174: 218-235.
[22] Lin L J, Liang Y C, Liu L, et al. Estimating PM2. 5 concentrations using the machine learning RF-XGBoost model in guanzhong urban agglomeration China[J]. Remote Sensing, 2022, 14(20): 14205239.
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