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

綦方中; 蔡阮昊; 曹柬

doi:doi:10.19912/j.0254-0096.tynxb.2025-0109

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

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

更新时间：2025-12-10

- 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
- Issue 11, Pages: 772-782(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2025-0109
  CLC： P183
- Published Online：04 December 2025，
  
  Published：04 December 2025
- 稿件说明：
移动端阅览
綦方中, 蔡阮昊, 曹柬. 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[J]. 2025, (11): 772-782.
DOI：

綦方中, 蔡阮昊, 曹柬. 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[J]. 2025, (11): 772-782. DOI： doi:10.19912/j.0254-0096.tynxb.2025-0109.

摘要

在结合传统卷积神经网络（CNN）和长短期记忆网络（LSTM）优势的基础上

提出一种基于循环DBSMOTE和多头注意力机制的风力发电机故障预警组合模型与应用体系。针对风力发电机运行数据集不平衡比率较高的问题

提出并使用一种循环DBSMOTE算法对数据集进行平衡处理。在组合预警模型中

使用CNN和LSTM算法来提取平衡处理后输入数据的空间维度特征和时间维度特征以增加数据挖掘的深度

同时引入多头注意力机制

极大地提升了模型的训练精度和预警性能。通过对采集自实际风场的风力发电机运行数据进行训练

模型在实验中实现了提前期长达32 h的故障预警能力。进一步地

通过消融实验、对比实验和十折交叉验证实验

表明所提出的组合预警模型具有较高的有效性和稳定性。

Abstract

This study proposes a hybrid fault early-warning model and architecture for wind turbines

integrating the advantages of traditional convolutional neural networks （CNN） and long short-term memory networks （LSTM）

coupled with a cyclic DBSMOTE algorithm and a multi-head attention mechanism. First

a cyclic DBSMOTE algorithm is developed to balance highly imbalanced wind turbine operational datasets

mitigating the bias caused by extreme class distribution. In the hybrid early-warning model

CNN and LSTM are employed to extract spatial and temporal features from the balanced data

respectively

enhancing the depth of data mining. Additionally

a multi-head attention mechanism is introduced to prioritize critical temporal-spatial correlations

significantly improving the model’s training accuracy and early-warning performance. Through hyperparameter optimization

the model achieves fault early-warning capabilities with a lead time of up to 32 hours in experimental validation. Further validation via ablation studies

comparative experiments

and ten-fold cross-validation demonstrates that the proposed hybrid model exhibits superior effectiveness and stability in wind turbine fault early warning.

关键词

Keywords

references

HUTCHINSON M, ZHAO F. Global wind report2023[R]. GWR23, 2023.

谷晓娇, 陈长征. 基于VMD和QPSO-SR的风力发电机轴承故障提取方法[J]. 太阳能学报, 2019, 40(10): 2946-2952.

LIAN J J, CAI O, DONG X F, et al.Health monitoring and safety evaluation of the offshore wind turbine structure: a review and discussion of future development[J]. Sustainability, 2019, 11(2): 494.

LECUN Y, BENGIO Y, HINTON G.Deep learning[J]. Nature, 2015, 521(7553): 436-444.

CASTELLANI F, ASTOLFI D, NATILI F.SCADA data analysis methods for diagnosis of electrical faults to wind turbine generators[J]. Applied sciences, 2021, 11(8): 3307.

丁显, 韩宁宁, 滕伟. 基于深度学习的直驱风力发电机故障辨识[J]. 可再生能源, 2018, 36(10): 1535-1540.

刘家瑞, 杨国田, 杨锡运. 基于深度卷积自编码器的风力发电机故障预警方法研究[J]. 太阳能学报, 2022, 43(11): 215-223.

郭俊锋, 王淼生, 孙磊, 等. 基于生成对抗网络的滚动轴承不平衡数据集故障诊断新方法[J]. 计算机集成制造系统, 2022, 28(9): 2825-2835.

李慧芳, 徐光浩, 黄双喜. 基于主动生成式过采样和深度堆叠网络的轴承故障诊断[J]. 计算机集成制造系统, 2023, 29(1): 146-159.

BUNKHUMPORNPAT C, SINAPIROMSARAN K, LURSINSAP C.DBSMOTE: density-based synthetic minority over-sampling TEchnique[J]. Applied intelligence, 2012, 36(3): 664-684.

王朋飞, 刘长良, 徐健, 等. 基于图注意力和时间卷积网络的风电齿轮箱故障预警方法[J]. 电子测量与仪器学报, 2023, 37(8): 204-213.

黄荣舟, 汤宝平, 杨燕妮, 等. 基于长短时记忆网络融合SCADA数据的风电齿轮箱状态监测[J]. 太阳能学报, 2021, 42(1): 235-239.

CHEN H S, LIU H, CHU X N, et al.Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network[J]. Renewable energy, 2021, 172: 829-840.

赵小强, 张亚洲. 利用改进卷积神经网络的滚动轴承变工况故障诊断方法[J]. 西安交通大学学报, 2021, 55(12): 108-118.

向玲, 王朋鹤, 李京蓄. 基于CNN-LSTM的风力发电机异常状态检测[J]. 振动与冲击, 2021, 40(22): 11-17.

KONG Z Q, TANG B P, DENG L, et al.Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units[J]. Renewable energy, 2020, 146: 760-768.

沈涛, 李舜酩. 针对滚动轴承故障的批标准化CNN-LSTM诊断方法[J]. 计算机集成制造系统, 2022, 28(12): 3946-3955.

王进花, 韩金玉, 曹洁, 等. 基于AM和CNN的多级特征融合的风力发电机轴承故障诊断方法[J]. 太阳能学报, 2024, 45(5): 51-61.

谭启瑜, 马萍, 张宏立, 等. 基于图注意力网络的风力发电机齿轮箱故障诊断[J]. 太阳能学报, 2024, 45(1): 265-274.

文靖杰, 王勇, 李金龙, 等. 多头自注意力机制的Faster R-CNN目标检测算法[J]. 现代电子技术, 2024, 47(7): 8-16.

张玉彦, 张永奇, 孙春亚, 等. 不平衡样本下基于生成式对抗网络的风机叶片开裂状态识别[J]. 计算机集成制造系统, 2023, 29(2): 532-543.

PAPPAS C, KOVAIOS S, MORALIS-PEGIOS M, et al.Programmable Tanh-, ELU-, Sigmoid-, and Sin-based Nonlinear Activation Functions for Neuromorphic Photonics[J]. IEEE journal of selected topics in quantum electronics, 2023, 29(6): 6101210.

CHUNG J Y, GULCEHRE C, CHO K-G, et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[C]//In Proceedings of the 2014 Neural Information Processing Systems(NeurIPS 2014) Deep Learning and Representation Learning Workshop (NeurIPS 2014). Montréal, Canada, 2014.

熊红林, 樊重俊, 赵珊, 等. 基于多尺度卷积神经网络的玻璃表面缺陷检测方法[J]. 计算机集成制造系统, 2020, 26(4): 900-909.

VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[C]//Advances in Neural Information Processing Systems 30 (NeurIPS 2017). Long Beach, California, USA, 2017: 5998-6008.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

YOLO-DSBF：一种新的太阳电池缺陷识别方法

基于相似日聚类和WOA-VMD-TCN-Transformer模型的短期光伏功率研究

基于并行架构网络与改进动态FD-KNN的风力发电机轴承故障预警

基于贝叶斯参数学习优化的海上风电机组动态可靠性预测

基于改进SVR-SSA-BiLSTM误差修正的短期风电功率混合预测模型研究

Related Author

何羿颉

楚瀛

夏能弘

江正源

李曦

赵丹阳

汤旭晶

汪恬

Related Institution

School of Civil Engineering, Hunan University of Science and Technology

School of Civil Engineering, Central South University

School of New Energy, Shenyang Institute of Engineering

Liaoning Solar Energy Research and Application Co., Ltd.

State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology

AI问答

⁰