RESEARCH ON FAULT WARNING METHOD OF WIND TURBINE GEARBOX BASED ON IICEEMDAN-PCA-GRU

Ma Yongguang; Feng Yongsheng

doi:10.19912/j.0254-0096.tynxb.2021-1465

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (4) : 67-73. DOI: 10.19912/j.0254-0096.tynxb.2021-1465

RESEARCH ON FAULT WARNING METHOD OF WIND TURBINE GEARBOX BASED ON IICEEMDAN-PCA-GRU

Ma Yongguang, Feng Yongsheng

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Abstract

In order to fully mine the hidden information of SCADA data, reduce the redundancy among features, and improve the prediction and warning accuracy of the model, a fault warning method for wind turbine gearbox was proposed based on the combination of fully noise-assisted aggregation empirical mode decomposition （IICEEMDAN）, principal component analysis （PCA） and gate recurrent unit（GRU）. Pearson correlation coefficient method was used for feature extraction, IICEEMDAN was used for feature decomposition to obtain the continuity signals of features in different time scales. PCA is used to extract the key factors of decomposition features as network training inputs. GRU network conducts modeling training on the input time series characteristics to predict the oil pool temperature of the gearbox. Statistical theory is used to analyze the error between the predicted value and the actual value of the oil pool temperature, and the early warning threshold is set according to the actual situation. Using sliding window theory to realize gearbox fault warning. A wind field in North China was used to verify the effectiveness of the proposed method for early fault warning of gearbox.

Key words

wind turbines / statistical method / feature extraction / GRU / fault warning / gearbox / sliding window

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Ma Yongguang, Feng Yongsheng. RESEARCH ON FAULT WARNING METHOD OF WIND TURBINE GEARBOX BASED ON IICEEMDAN-PCA-GRU[J]. Acta Energiae Solaris Sinica. 2023, 44(4): 67-73 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1465

References

[1] 王超, 李大中. 基于LSTM网络的风机齿轮箱轴承故障预警[J]. 电力科学与工程, 2020, 36(9): 40-45.
WANG C, LI D Z.Fault early warning of fan gearbox bearing based on LSTM network[J]. Electric power science and engineering, 2020, 36(9): 40-45.
[2] 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.
HUANG R Z, TANG B P, YANG Y N, et al.Condition monitoring of wind turbine gearbox based on LSTM neural network fusing SCADA data[J]. Acta energiae solaris sinica, 2021, 42(1): 235-239.
[3] 魏书荣, 张鑫, 符杨, 等. 基于GRA-LSTM-Stacking模型的海上双馈风力发电机早期故障预警与诊断[J]. 中国电机工程学报, 2021, 41(7): 2373-2382.
WEI S R, ZHANG X, FU Y, et al.Early fault warning and diagnosis of offshore wind DFIG based on GRA-LSTM-Stacking model[J]. Proceedings of the CSEE, 2021, 41(7): 2373-2382.
[4] 吕凤霞, 缪益, 别锋锋, 等. ICEEMDAN和GS-SVM算法在滚动轴承声发射故障诊断中的应用研究[J]. 噪声与振动控制, 2022, 42(6): 92-97, 129.
LYU F X, MIAO Y, BIE F F, et al.Application of ICEEMDAN and GS-SVM algorithm in the fault diagnosis of acoustic emission of rolling bearings[J]. Noise and vibration control, 2022, 42(6): 92-97, 129.
[5] 陈东峰. 基于ICEEMDAN和SSA-ELM的短期风电功率预测研究[D]. 保定: 华北电力大学, 2021.
CHEN D F.Research on short-term wind power prediction based on ICEEMDAN and SSA-ELM[D]. Baoding:North China Electric Power University, 2021.
[6] 别锋锋, 都腾飞, 庞明军, 等. 基于ICEEMDAN-GRNN神经网络的往复泵故障诊断方法研究[J]. 机械设计与制造, 2021(3): 127-131.
BIE F F, DU T F, PANG M J, et al.Research on fault diagnosis method of reciprocating pump based on ICEEMDAN-GRNN neural network[J]. Mechanical design and manufacturing, 2021(3): 127-131.
[7] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM 的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[8] 李俊卿, 陈雅婷. LSTM-CNN网络在同步电机励磁绕组匝间短路故障预警中的应用[J]. 华北电力大学学报(自然科学版), 2020, 47(4): 61-70.
LI J Q,CHEN Y T.Application of LSTM-CNN network in fault waring of inter-turn short circuit in field windings of synchronous generator[J]. Journal of North China Electric Power University(natural cience edition), 2020, 47(4): 61-70.
[9] 张国珍, 王其乐, 叶天泽, 等. 基于随机森林和LSTM-自编码算法的风机高温降容状态智能检测方法[J]. 华北电力大学学报(自然科学版), 2021, 48(3): 81-88.
ZHANG G Z, WANG Q L, YE T Z, et al.Intelligent detection method of high-temperature capacity reduction of wind turbine based on random forest and LSTM-autoencoder[J]. Journal of North China Electric Power University (natural science edition), 2021, 48(3): 81-88.
[10] 乔福宇, 马良玉, 马永光. 基于功率曲线分析与神经网络的风电机组故障预警方法[J]. 中国测试, 2020, 46(8): 44-50.
QIAO F Y, MA L Y, MA Y G.Wind turbine fault early warning method based on power curve analysis and neural network[J]. China measurement & testing, 2020, 46(8): 44-50.
[11] 刘长良, 王梓齐. 基于MSET和集成学习的风电机组齿轮箱故障预警[J]. 太阳能学报, 2020, 41(11): 228-233.
LIU C L, WANG Z Q.Fault early warning of wind turbine gearbox based on MSET and ensemble learnING[J]. Acta energiae solaris sinica, 2020, 41(11): 228-233.
[12] 赵洪山, 刘辉海. 基于深度学习网络的风电机组主轴承故障检测[J]. 太阳能学报, 2018, 39(3):588-595.
ZHAO H S, LIU H H.Fault detection of wind turbine main bearing based on deep learning network[J]. Acta energiae solaris sinica, 2018, 39(3): 588-595.