Universal Generating Function(UGF)techniques have been applied to Multi-State System(MSS)reliability analysis,such as long term reserve expansion of power systems with high wind power penetration.However,using simple ...Universal Generating Function(UGF)techniques have been applied to Multi-State System(MSS)reliability analysis,such as long term reserve expansion of power systems with high wind power penetration.However,using simple steady-state distribution models for wind power and large generating units in reliability assessment can yield pessimistic appraisals.To more accurately assess the power system reliability,UGF techniques are extended to dynamic probabilistic simulation analysis on two aspects of modelling improvement.Firstly,a principal component analysis(PCA)combined with a hierarchal clustering algorithm is used to achieve the salient and time-varying patterns of wind power,then a sequential UGF equivalent model of wind power output is established by an apportioning method.Secondly,other than the traditional two-state models,the conventional generator UGF equivalent model is established as a four discrete-state continuous-time Markov model by Lztransform.In the construction process of such a UGF model,the state values are transformed into the integral multiples of one common factor by choosing proper common factors,thus effectively restraining the exponential growth of its state number and alleviating the explosion thereof.The method is suitable for reliability assessment with dynamic probabilistic distributed random variables.In addition,by acquiring the clustering information of wind power,the system reliability indices,such as fuel cost and CO_(2) emissions through different seasons and on different workdays,are calculated.Finally,the effectiveness of the method is verified by a modified IEEE-RTS 79 system integrated with several wind farms of historical hourly wind power data of Zhangbei wind farm in North China.展开更多
In practical engineering,sometimes the probability density functions( PDFs) of stress and strength can not be exactly determined,or only limited experiment data are available. In these cases,the traditional stress-str...In practical engineering,sometimes the probability density functions( PDFs) of stress and strength can not be exactly determined,or only limited experiment data are available. In these cases,the traditional stress-strength interference( SSI) model based on classical probabilistic approach can not be used to evaluate reliabilities of components. To solve this issue, the traditional universal generating function( UGF) is introduced and then it is extended to represent the discrete interval-valued random variable.Based on the extended UGF,an improved discrete interval-valued SSI model is proposed, which has higher calculation precision compared with the existing methods. Finally,an illustrative case is given to demonstrate the validity of the proposed model.展开更多
基金This work was supported by the National High Technology Research and Development Program of China(863 Program)(No.2011AA05A101)National Natural Science Foundation of China(No.51177092).
文摘Universal Generating Function(UGF)techniques have been applied to Multi-State System(MSS)reliability analysis,such as long term reserve expansion of power systems with high wind power penetration.However,using simple steady-state distribution models for wind power and large generating units in reliability assessment can yield pessimistic appraisals.To more accurately assess the power system reliability,UGF techniques are extended to dynamic probabilistic simulation analysis on two aspects of modelling improvement.Firstly,a principal component analysis(PCA)combined with a hierarchal clustering algorithm is used to achieve the salient and time-varying patterns of wind power,then a sequential UGF equivalent model of wind power output is established by an apportioning method.Secondly,other than the traditional two-state models,the conventional generator UGF equivalent model is established as a four discrete-state continuous-time Markov model by Lztransform.In the construction process of such a UGF model,the state values are transformed into the integral multiples of one common factor by choosing proper common factors,thus effectively restraining the exponential growth of its state number and alleviating the explosion thereof.The method is suitable for reliability assessment with dynamic probabilistic distributed random variables.In addition,by acquiring the clustering information of wind power,the system reliability indices,such as fuel cost and CO_(2) emissions through different seasons and on different workdays,are calculated.Finally,the effectiveness of the method is verified by a modified IEEE-RTS 79 system integrated with several wind farms of historical hourly wind power data of Zhangbei wind farm in North China.
基金National Natural Science Foundation of China(No.51265025)
文摘In practical engineering,sometimes the probability density functions( PDFs) of stress and strength can not be exactly determined,or only limited experiment data are available. In these cases,the traditional stress-strength interference( SSI) model based on classical probabilistic approach can not be used to evaluate reliabilities of components. To solve this issue, the traditional universal generating function( UGF) is introduced and then it is extended to represent the discrete interval-valued random variable.Based on the extended UGF,an improved discrete interval-valued SSI model is proposed, which has higher calculation precision compared with the existing methods. Finally,an illustrative case is given to demonstrate the validity of the proposed model.
