复杂工况条件下多头注意力双向长短时记忆网络的风电机组缺失数据修复方法研究

doi:10.3901/JME.2023.14.001

机械工程学报 ›› 2023, Vol. 59 ›› Issue (14): 1-9.doi: 10.3901/JME.2023.14.001

扫码分享

复杂工况条件下多头注意力双向长短时记忆网络的风电机组缺失数据修复方法研究

余晓霞¹, 汤宝平¹, 王伟影², 吴宣勇¹, 李彪¹

1. 重庆大学机械传动国家重点实验室重庆 400044;
2. 中船重工龙江广瀚燃气轮机有限公司哈尔滨 150078

收稿日期:2022-01-25 修回日期:2022-07-18 出版日期:2023-07-20 发布日期:2023-08-16
通讯作者: 汤宝平(通信作者),男,1971年出生,博士,教授,博士研究生导师。主要研究方向为机电装备安全服役与退化趋势、测试计量技术及仪器、无线传感器网络等。E-mail:bptang@cqu.edu.cn
作者简介:余晓霞,男,1993年出生,博士研究生。主要研究方向为旋转机械的健康监测、退化趋势预测以及故障识别等。E-mail:xiaoxiayull@hotmail.com
基金资助:
国家重点研发计划(2020YFB1709800)和重庆市自然科学重点基金(cstc2019jcyj-zdxmX0026)资助项目。

Repairing Deteriorated Data of Wind Turbines by Multi-head Attention Bi-directional Long Short Time Memory Networks under Complex Working Conditions

YU Xiaoxia¹, TANG Baoping¹, WANG Weiying², WU Xuanyong¹, LI Biao¹

1. The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044;
2. CSSC Longjiang Guanghan Gas Turbine Co., Ltd., Harbin 150078

Received:2022-01-25 Revised:2022-07-18 Online:2023-07-20 Published:2023-08-16

摘要/Abstract

摘要： 针对变速变载条件下风电机组监测参量中包含大量缺失数据导致基于深度学习的状态监测模型预警精度低的难题，提出一种多头注意力双向长短时记忆网络缺失数据修复方法(Multi-headed attention bidirectional long and short term memory network, MA-BiLSTM)。所提方法利用多头注意力机制抑制复杂工况条件下变速变载对神经网络特征提取时的干扰，采用跨层连接残差单元增加模型的特征提取能力，充分学习已有监测数据的隐藏特征以及多源参量间的关联关系；采用双向长短时记忆网络同时对风电机组监测数据的复杂变化规律进行学习，实现监测参量中缺失数据的预测修复。实例应用结果表明所提多头注意力双向长短时记忆网络能够抑制复杂工况条件下的变速变载干扰，实现单变量或多变量中缺失数据的预测修复，有效提升风电机组状态监测精度。

关键词: 风电机组, 缺失数据预测, 多头注意力机制, 双向长短时记忆网络

Abstract: To address the problem of low early warning accuracy of condition monitoring models due to a large number of missing data in wind turbine monitoring parameters under variable speed and variable load, multi-headed attention bidirectional long and short-term memory networks (MA-BiLSTM) is proposed for repair those data. The multi-headed attention machine is used to suppress the interference of variable loads on the neural network feature extraction under complex working conditions. In addition, the model feature extraction ability is increased by constructing a cross-layer of residual units, and the hidden features of existing monitoring data and the correlation between multi-source parameters are fully learned. The Bi-LSTM cells are used to simultaneously learn the law of the monitoring data of wind turbines to achieve the prediction and repair of incomplete data. The application results show that the proposed MA-BiLSTM networks can suppress the multivariate load disturbance under complex working conditions and realize the repair of incomplete data for improving fault detection accuracy of wind turbines.

Key words: wind turbine, deteriorated data repair, multi-headed attention mechanism, bi-directional long and short term memory network

中图分类号:

TH17

余晓霞, 汤宝平, 王伟影, 吴宣勇, 李彪. 复杂工况条件下多头注意力双向长短时记忆网络的风电机组缺失数据修复方法研究[J]. 机械工程学报, 2023, 59(14): 1-9.

YU Xiaoxia, TANG Baoping, WANG Weiying, WU Xuanyong, LI Biao. Repairing Deteriorated Data of Wind Turbines by Multi-head Attention Bi-directional Long Short Time Memory Networks under Complex Working Conditions[J]. Journal of Mechanical Engineering, 2023, 59(14): 1-9.

参考文献

[1] 张帆,刘德顺,戴巨川,等. 一种基于SCADA参数关系的风电机组运行状态识别方法[J]. 机械工程学报,2019,55(4):1-9. ZHANG Fan,LIU Deshun,DAI Juchuan,et al. An operating condition recognition method of wind turbine based on SCADA parameter relations[J]. Journal of Mechanical Engineering,2019,55(4):1-9.
[2] WANG Jinjiang,LIANG Yuanyuan,ZHENG Yinghao,et al. An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples[J]. Renewable Energy,2020,145:642-650.
[3] TENG Wei,DING Xian,CHENG Hao,et al. Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform[J]. Renewable Energy,2019,136:393-402.
[4] 雷亚国,许学方,蔡潇,等. 面向机械装备健康监测的数据质量保障方法研究[J]. 机械工程学报,2021,57(4):1-9. LEI Yaguo,XU Xuefang,CAI Xiao,et al. Research on data quality assurance for health condition monitoring of machinery[J]. Journal of Mechanical Engineering,2021,57(4):1-9.
[5] VELASCO-GALLEGO C,LAZAKIS I. Real-time data-driven missing data imputation for short-term sensor data of marine systems. A comparative study[J]. Ocean Engineering,2020,218:108261.
[6] KAISER J. Dealing with missing values in data[J]. Journal of Systems Integration,2014,5(1):42-51.
[7] LI D,DEOGUN J,SPAULDING W,et al. Towards missing data imputation:a study of fuzzy k-means clustering method[C]//International Conference on Rough Sets and Current Trends in Computing. Springer,Berlin,Heidelberg,2004:573-579.
[8] WANG Guanjin,LU Jie,CHOI K,et al. A transfer-based additive LS-SVM classifier for handling missing data[J]. IEEE Transactions on Cybernetics,2018,50(2):739-752.
[9] HOU K,XIA D,LI Q,et al. A two-stage ensemble of diverse models for recognition of abnormal data in raw wind data[C]//2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE,2016:880-884.
[10] ZHANG Y,THORBURN P. A dual-head attention model for time series data imputation[J]. Computers and Electronics in Agriculture,2021,189:106377.
[11] 张晟斐,李天梅,胡昌华,等. 基于深度卷积生成对抗网络的缺失数据生成方法及其在剩余寿命预测中的应用[J].航空学报,2021,42(6):625708. ZHANG Shengfei,LI Tianmei,HU Changhua,et al. Deep convolutional generative adversarial network based missing data generation method and its application in remaining useful life prediction[J]. Acta Aeronautica et Astronautica Sinica,2021,42(6):625708.
[12] AWAN S E,BENNAMOUN M,SOHEL F,et al. Imputation of missing data with class imbalance using conditional generative adversarial networks[J]. Neurocomputing,2021,453:164-171.
[13] QU Fuming,LIU Jinhai,MA Yanjuan,et al. A novel wind turbine data imputation method with multiple optimizations based on GANs[J]. Mechanical Systems and Signal Processing,2020,139:106610.
[14] LI Dong,LI Linhao,LI Xianling,et al. A spatio-temporal deep model for multiple time-series missing imputation[J]. Neurocomputing,2020,411:351-363.
[15] FURFARI F A. The transformer[J]. IEEE Industry Applications Magazine,2002,8:8-15.
[16] TAY Y,DEHGHANI M,BAHRI D,et al. Efficient transformers:A survey[J]. ACM Computing Surveys,2020,55(6):1-28.
[17] WANG Zhiwei,MA Yao,LIU Zitao,et al. R-transformer:Recurrent neural network enhanced transformer[J]. arXiv preprint arXiv,2019:1-11.
[18] AZEVEDO H D M,ARAÚJO A M,BOUCHONNEAU N. A review of wind turbine bearing condition monitoring:State of the art and challenges[J]. Renewable and Sustainable Energy Reviews,2016,56:368-379.
[19] RENSTRÖM N,BANGALORE P,HIGHCOCK E. System-wide anomaly detection in wind turbines using deep autoencoders[J]. Renewable Energy,2020,157:647-659.
[20] WANG S,ZHANG Z,WANG P,et al. Failure warning of gearbox for wind turbine based on 3σ-median criterion and NSET[J]. Energy Reports,2021,7:1182-1197.

复杂工况条件下多头注意力双向长短时记忆网络的风电机组缺失数据修复方法研究

Repairing Deteriorated Data of Wind Turbines by Multi-head Attention Bi-directional Long Short Time Memory Networks under Complex Working Conditions

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	朱永超, 朱才朝, 谭建军, 冉峯, 宋朝省. 考虑数据特征差异的风电齿轮箱群组健康状态预测研究[J]. 机械工程学报, 2024, 60(18): 64-75.
[2]	张晓英, 吴宏强, 舒子江, 王琨, 陈伟. 基于PCA和改进ABC-K聚类算法的双馈机组风电场等值建模^*[J]. 电气工程学报, 2023, 18(1): 201-210.
[3]	谭建军, 朱才朝, 李浩, 宋朝省, 董晔弘. 基础运动对漂浮式风电机组齿轮箱传动系统附加激励的影响[J]. 机械工程学报, 2023, 59(1): 35-49.
[4]	肖钊, 邓杰文, 刘晓明, 段书用, 许守亮. 基于运行规律和TICC算法的风电SCADA高维时序数据聚类方法[J]. 机械工程学报, 2022, 58(23): 196-207.
[5]	车畅畅, 王华伟, 倪晓梅, 蔺瑞管, 熊明兰. 基于1D-CNN和Bi-LSTM的航空发动机剩余寿命预测[J]. 机械工程学报, 2021, 57(14): 304-312.
[6]	王昊, 郝万君, 李泽, 崔国增, 吴宇, 张正夫, 孙志辉. 基于滑模观测器的风力机故障快速重构方法设计[J]. 机械工程学报, 2021, 57(14): 261-269,281.
[7]	董兴辉, 李佳, 高迪, 郑凯. 风电机组效能和性能统一评估模型研究[J]. 机械工程学报, 2021, 57(14): 253-260.
[8]	党存禄, 李旭鹏, 孙海斌, 武文成. 双馈风电机组次同步控制相互作用的抑制方法 ^*[J]. 电气工程学报, 2021, 16(1): 119-126.
[9]	张帆, 刘德顺, 戴巨川, 王超, 沈祥兵. 一种基于SCADA参数关系的风电机组运行状态识别方法[J]. 机械工程学报, 2019, 55(4): 1-9.
[10]	包广清,李媛,马明,汪宁渤. 减载运行方式下直驱永磁风电机组虚拟同步控制策略 ^*[J]. 电气工程学报, 2019, 14(3): 47-53.
[11]	陈明,陈永军,李力森. 变频柜在风电机组塔底工作的散热研究[J]. 电气工程学报, 2018, 13(5): 33-39.
[12]	董兴辉, 张鑫淼, 张光, 王帅. 基于云模型的风电机组输出功率特性分析[J]. 机械工程学报, 2017, 53(22): 198-205.
[13]	沈小军,周冲成,吕洪. 大型风电场风电机组分组方法研究综述[J]. 电气工程学报, 2016, 11(5): 1-10.
[14]	戴巨川. 叶片断裂事故条件下直驱式风电机组动态特性分析[J]. , 2013, 49(2): 190-198.
[15]	郭建英;孙永全;王铭义;丁喜波. 基于计算机仿真的风电机组系统可靠性综合[J]. , 2012, 48(2): 2-8.