基于解析冗余关系理论的电力电子电路健康预测
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摘要
讨论了电力电子电路中元器件级的健康预测。元器件级的剩余使用寿命是通过反推残差信号方法得到的。首先利用键合图建立系统的动态模型,得到全局解析冗余关系,通过故障特征矩阵进行故障检测与隔离;然后与元器件的退化模型相结合的方法,得到元器件相对应的残差退化曲线,从而获得元器件退化过程数据样本。根据元器件的等效电阻与残差的退化关系,结合故障阈值与失效值和极限学习机(extreme learning machine,ELM)算法可计算出元器件级的剩余使用时间。最后将该方法应用于典型电力电子电路Buck电路中,在20-sim软件和Matlab仿真环境中进行联合仿真,验证了此方法的有效性。
The health prediction of component level in dynamic system is discussed. The residual service life of the component level is obtained by means of inverse residual signal. Firstly,the dynamic model of the system is established by using the bond graph,and the global analytical redundancy is obtained,and the fault detection and isolation is carried out by fault feature matrix. Then,the residual degradation curve corresponding to the components is obtained by combining the degradation mechanism of the components,so as to obtain the data samples of the degradation process of the components. According to the relationship between the equivalent resistance and residual degradation of components,the remaining use time of component level can be calculated by combining the fault threshold with the failure value and the extreme learning machine( ELM) algorithm. Finally,this method is applied to the typical power electronic circuit Buck circuit,and the simulation is carried out in the 20-sim software and Matlab simulation environment to verify the effectiveness of this method.
The health prediction of component level in dynamic system is discussed. The residual service life of the component level is obtained by means of inverse residual signal. Firstly,the dynamic model of the system is established by using the bond graph,and the global analytical redundancy is obtained,and the fault detection and isolation is carried out by fault feature matrix. Then,the residual degradation curve corresponding to the components is obtained by combining the degradation mechanism of the components,so as to obtain the data samples of the degradation process of the components. According to the relationship between the equivalent resistance and residual degradation of components,the remaining use time of component level can be calculated by combining the fault threshold with the failure value and the extreme learning machine( ELM) algorithm. Finally,this method is applied to the typical power electronic circuit Buck circuit,and the simulation is carried out in the 20-sim software and Matlab simulation environment to verify the effectiveness of this method.
引文
1 Sikorska J Z,Hodkiewicz M,Ma L. Prognostic modelling options for remaining useful life estimation by industry[J]. Mechanical Systems&Signal Processing,2011,25(5):1803-1836
2 Tobon-Mejia D A,Medjaher K,Zerhouni N. CNC machine tool's wear diagnostic and prognostic by using dynamic bayesian networks[J]. Mechanical Systems&Signal Processing,2012,28:167-182
3 Heng A,Tan A C C,Mathew J,et al. Intelligent condition-based prediction of machinery reliability[J]. Mechanical Systems and Signal Processing,2009,23(5):1600-1614
4 Saha B,Kai G,Christophersen J. Comparison of prognostic algorithms for estimating remaining useful life of batteries[J]. Transactions of the Institute of Measurement&Control,2009,31(3):293-308
5 Jouin M,Gouriveau R,Hissel D,et al. Prognostics of PEM fuel cell in a particle filtering framework[J]. International Journal of Hydrogen Energy,2014,39(1):481-494
6 Yu M,Wang D,Luo M,et al. Prognosis of hybrid systems with multiple incipient faults:augmented global analytical redundancy relations approach[J]. IEEE Transactions on Systems Man&Cybernetics Part A Systems&Humans,2011,41(3):540-551
7 金洲.基于能量模型的参数不确定性混杂系统故障诊断方法研究[D].乌鲁木齐:新疆大学,2017Jin Zhou. Research on fault diagnosis method of parametric hybrid system based on energy model[D]. Urumqi:Xinjiang University,2017
8 张歆炀.基于模型的风力发电并网三电平逆变器参数性故障诊断的研究[D].乌鲁木齐:新疆大学,2016Zhang Xinyang. Based on the model of the wind power generation and study of three parameters of fault diagnosis level inverter[D]. Urumqi:Xinjiang University,2016
9 Huang G B,Zhu Q Y,Siew C K. Extreme learning machine:Theory and applications[J]. Neurocomputing,2006,70(1-3):489-501
10 Venet P,Lahyani A,Grellet G. Influence of aging on electrolytic capacitors function in static converters:Fault prediction method[J].The European Physical Journal-Applied Physics,2008,5(1):71-83
11 陈妤.电力电子电路参数辨识新方法与故障预测算法研究[D].南京:南京航空航天大学,2012Chen Yu. A new method for parameter identification of power electronic circuits and fault prediction algorithm[D]. Nanjing:Nanjing University of Aeronautics and Astronautics,2012
12 帕孜来·马合木提,刘文红.基于区间解析冗余关系的参数不确定性的仿真研究[J].科学技术与工程,2018,18(1):261—265Pazilai Mahemuti,Liu Wenhong. Simulation research on parameter uncertainty based on interval analytic redundancy relationship[J].Science Technology and Engineering,2018,18(1):261-265
2 Tobon-Mejia D A,Medjaher K,Zerhouni N. CNC machine tool's wear diagnostic and prognostic by using dynamic bayesian networks[J]. Mechanical Systems&Signal Processing,2012,28:167-182
3 Heng A,Tan A C C,Mathew J,et al. Intelligent condition-based prediction of machinery reliability[J]. Mechanical Systems and Signal Processing,2009,23(5):1600-1614
4 Saha B,Kai G,Christophersen J. Comparison of prognostic algorithms for estimating remaining useful life of batteries[J]. Transactions of the Institute of Measurement&Control,2009,31(3):293-308
5 Jouin M,Gouriveau R,Hissel D,et al. Prognostics of PEM fuel cell in a particle filtering framework[J]. International Journal of Hydrogen Energy,2014,39(1):481-494
6 Yu M,Wang D,Luo M,et al. Prognosis of hybrid systems with multiple incipient faults:augmented global analytical redundancy relations approach[J]. IEEE Transactions on Systems Man&Cybernetics Part A Systems&Humans,2011,41(3):540-551
7 金洲.基于能量模型的参数不确定性混杂系统故障诊断方法研究[D].乌鲁木齐:新疆大学,2017Jin Zhou. Research on fault diagnosis method of parametric hybrid system based on energy model[D]. Urumqi:Xinjiang University,2017
8 张歆炀.基于模型的风力发电并网三电平逆变器参数性故障诊断的研究[D].乌鲁木齐:新疆大学,2016Zhang Xinyang. Based on the model of the wind power generation and study of three parameters of fault diagnosis level inverter[D]. Urumqi:Xinjiang University,2016
9 Huang G B,Zhu Q Y,Siew C K. Extreme learning machine:Theory and applications[J]. Neurocomputing,2006,70(1-3):489-501
10 Venet P,Lahyani A,Grellet G. Influence of aging on electrolytic capacitors function in static converters:Fault prediction method[J].The European Physical Journal-Applied Physics,2008,5(1):71-83
11 陈妤.电力电子电路参数辨识新方法与故障预测算法研究[D].南京:南京航空航天大学,2012Chen Yu. A new method for parameter identification of power electronic circuits and fault prediction algorithm[D]. Nanjing:Nanjing University of Aeronautics and Astronautics,2012
12 帕孜来·马合木提,刘文红.基于区间解析冗余关系的参数不确定性的仿真研究[J].科学技术与工程,2018,18(1):261—265Pazilai Mahemuti,Liu Wenhong. Simulation research on parameter uncertainty based on interval analytic redundancy relationship[J].Science Technology and Engineering,2018,18(1):261-265