风电机组轴承健康劣化趋势建模与仿真
|
摘要
以风电机组轴承为研究对象,利用SCADA(Supervisory Control And Data Acquisition)监测参数,应用最小二乘曲面拟合算法,建立轴承温度健康状态劣化趋势模型。改进并应用EEMD(Ensemble Empirical Mode Decomposition)方法,分解具有非平稳性特性的轴承劣化趋势为一系列相对平稳的分量,利用时间序列神经网络分别对各分量单独预测,叠加所有分量的预测值作为最终的预测结果。经过仿真测试,该方法能够以更高的精度预测风电机组轴承健康状态劣化趋势。
By taking a wind turbine bearing as research object,the model of bearing temperature health's degradation trend is established through using least squares surface fitting and the monitored parameters from Supervisory Control And Data Acquisition(SCADA).Bearings' degradation trend with unsteady characteristics is decomposed by modified Ensemble Empirical Mode Decomposition(EEMD) to obtain several relatively steady components.Components are predicted respectively by time series neural network and the predicted results of all the components are added to obtain final prediction result.Comprehensive simulations and comparisons show that the proposed method can predict the health degradation trend of wind turbine bearings with higher accuracy.
By taking a wind turbine bearing as research object,the model of bearing temperature health's degradation trend is established through using least squares surface fitting and the monitored parameters from Supervisory Control And Data Acquisition(SCADA).Bearings' degradation trend with unsteady characteristics is decomposed by modified Ensemble Empirical Mode Decomposition(EEMD) to obtain several relatively steady components.Components are predicted respectively by time series neural network and the predicted results of all the components are added to obtain final prediction result.Comprehensive simulations and comparisons show that the proposed method can predict the health degradation trend of wind turbine bearings with higher accuracy.
引文
[1]李辉,胡姚刚,唐显虎,等.并网风电机组在线运行状态评估方法[J].中国电机工程学报,2010,30(33):103-109.Li Hui,Hu Yaogang,Tang Xianhu,et al.Method for on-line operating conditions assessment for a grid-connected wind turbine generator system[J].Proceedings of the CSEE,2010,30(33):103-109.
[2]Tchakoua P,Wamkeue R,Ouhrouche M,et al.Wind turbine condition monitoring:state-of-the-art review new trends and future challenges[J].Energies(S1996-1073),2014,7(4):2595-2630.
[3]YANG W,TAVNER P J,CRABTREE C J,et al.Cost-effective condition monitoring for wind turbines[J].IEEE Transaction on Industrial Electronics(S0278-0046),2010,57(1):263-271.
[4]苏连成,李兴林,李小但,等.风电机组轴承的状态监测和故障诊断与运行维护[J].轴承,2012(1):47-53.Su Liancheng,Li Xinglin,Li Xiaoli,et al.Condition monitoring and fault diagnosis and operating maintenancesystems for wind turbine bearings[J].Bearing,2012(1):47-53.
[5]彭进,王维庆,王海云,等.基于EEMD峭度-相关系数准则的多特征量风电机组轴承故障诊断[J].可再生能源,2016,34(10):1481-1490.Peng Jin,Wang Weiqing,Wang Haiyun,et al.Fault diagnosis method of wind turbine's bearing based on EEMD kurtosis-correlation coefficients criterion and multiple features[J].Renewable Energy,2016,34(10):1481-1490.
[6]赵洪山,李浪.基于MCKD-EMD的风电机组轴承早期故障诊断方法[J].电力自动化设备,2017,37(2):29-36.Zhao Hongshan,Li Lang.Incipient bearing fault diagnosis based on MCKD-EMD for wind turbine[J].Electric Power Automation Equipment,2017,37(2):29-36.
[7]安学利,唐拥军,王允.基于健康样本的风电机组滚动轴承健康状态评估[J].中国水利水电科学研究院学报,2015,13(1):48-53.An Xueli,Tang Yongjun,Wang Yun.State evalution of rolling bearings for wind turbines based on health samples[J].Journal of China Institute of Water Resources and Hydropower Research,2015,13(1):48-53.
[8]李辉,胡姚刚,李洋,等.基于温度特征量的风电轴承关键部件劣化渐变概率分析[J].电力自动化设备,2015,35(11):1-7.Li Hui,Hu Yaogang,Li Yang,et al.Gradual deterioration probability analysis based on temperature characteristic parameters or critical components of wind turbine generator system[J].Electric Power Automation Equipment,2015,35(11):1-7
[9]李大中,常城,许炳坤.基于样本优化的风电机组齿轮箱轴承温度预测[J].系统仿真学报,2017,29(2):374-380.Li Dazhong,Chang Cheng,Xu Bingkun.Wind Turbine Gearing Temperature Prediction Based on Sample Optimization[J].Journal of System Simulation,2017,29(2):374-380.
[10]王贺,胡志坚,张翌晖,等.基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测[J].电工技术学报,2014,29(4):237-245.Wang He,Hu Zhijian,Zhang Yihui,et al.A hybrid model for short-term wind speed forecasting based on ensemble empirical mode decomposition and least squares support vector machines[J].Transactions of China Electrotechnical Society,2014,29(4):237-245.
[11]An X,Jiang D,Li S,et al.Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine[J].Energy(S0360-5442),2011,36(9):5508-5520.
[12]Wong C X,Worden K.Generalised NARX shunting neural network modelling of friction[J].Mechanloal Systems and Signal Processing(S0888-3270),2007,21(1):553-572.
[13]霍晓程,李小平.用最小二乘法拟合曲面方程[J].赤峰学院学报,2009,25(6):11-13.Huo Xiaocheng,Li Xiaoping.Using the least squares fitting surface equation[J].Journal of Chifeng institute,2009,25(6):11-13.
[14]崔玉敏,和卫星,高彩虹.基于小波变换及HHT的轴承故障诊断应用研究[J].计算机仿真,2010,27(10):294-300.Cui Yumin,He Weixing,Gao Caihong.Application of bearing fault diagnosis based on wavelet and HHT[J].Computer Simultion,2010,27(10):294-300.
[2]Tchakoua P,Wamkeue R,Ouhrouche M,et al.Wind turbine condition monitoring:state-of-the-art review new trends and future challenges[J].Energies(S1996-1073),2014,7(4):2595-2630.
[3]YANG W,TAVNER P J,CRABTREE C J,et al.Cost-effective condition monitoring for wind turbines[J].IEEE Transaction on Industrial Electronics(S0278-0046),2010,57(1):263-271.
[4]苏连成,李兴林,李小但,等.风电机组轴承的状态监测和故障诊断与运行维护[J].轴承,2012(1):47-53.Su Liancheng,Li Xinglin,Li Xiaoli,et al.Condition monitoring and fault diagnosis and operating maintenancesystems for wind turbine bearings[J].Bearing,2012(1):47-53.
[5]彭进,王维庆,王海云,等.基于EEMD峭度-相关系数准则的多特征量风电机组轴承故障诊断[J].可再生能源,2016,34(10):1481-1490.Peng Jin,Wang Weiqing,Wang Haiyun,et al.Fault diagnosis method of wind turbine's bearing based on EEMD kurtosis-correlation coefficients criterion and multiple features[J].Renewable Energy,2016,34(10):1481-1490.
[6]赵洪山,李浪.基于MCKD-EMD的风电机组轴承早期故障诊断方法[J].电力自动化设备,2017,37(2):29-36.Zhao Hongshan,Li Lang.Incipient bearing fault diagnosis based on MCKD-EMD for wind turbine[J].Electric Power Automation Equipment,2017,37(2):29-36.
[7]安学利,唐拥军,王允.基于健康样本的风电机组滚动轴承健康状态评估[J].中国水利水电科学研究院学报,2015,13(1):48-53.An Xueli,Tang Yongjun,Wang Yun.State evalution of rolling bearings for wind turbines based on health samples[J].Journal of China Institute of Water Resources and Hydropower Research,2015,13(1):48-53.
[8]李辉,胡姚刚,李洋,等.基于温度特征量的风电轴承关键部件劣化渐变概率分析[J].电力自动化设备,2015,35(11):1-7.Li Hui,Hu Yaogang,Li Yang,et al.Gradual deterioration probability analysis based on temperature characteristic parameters or critical components of wind turbine generator system[J].Electric Power Automation Equipment,2015,35(11):1-7
[9]李大中,常城,许炳坤.基于样本优化的风电机组齿轮箱轴承温度预测[J].系统仿真学报,2017,29(2):374-380.Li Dazhong,Chang Cheng,Xu Bingkun.Wind Turbine Gearing Temperature Prediction Based on Sample Optimization[J].Journal of System Simulation,2017,29(2):374-380.
[10]王贺,胡志坚,张翌晖,等.基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测[J].电工技术学报,2014,29(4):237-245.Wang He,Hu Zhijian,Zhang Yihui,et al.A hybrid model for short-term wind speed forecasting based on ensemble empirical mode decomposition and least squares support vector machines[J].Transactions of China Electrotechnical Society,2014,29(4):237-245.
[11]An X,Jiang D,Li S,et al.Application of the ensemble empirical mode decomposition and Hilbert transform to pedestal looseness study of direct-drive wind turbine[J].Energy(S0360-5442),2011,36(9):5508-5520.
[12]Wong C X,Worden K.Generalised NARX shunting neural network modelling of friction[J].Mechanloal Systems and Signal Processing(S0888-3270),2007,21(1):553-572.
[13]霍晓程,李小平.用最小二乘法拟合曲面方程[J].赤峰学院学报,2009,25(6):11-13.Huo Xiaocheng,Li Xiaoping.Using the least squares fitting surface equation[J].Journal of Chifeng institute,2009,25(6):11-13.
[14]崔玉敏,和卫星,高彩虹.基于小波变换及HHT的轴承故障诊断应用研究[J].计算机仿真,2010,27(10):294-300.Cui Yumin,He Weixing,Gao Caihong.Application of bearing fault diagnosis based on wavelet and HHT[J].Computer Simultion,2010,27(10):294-300.