To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on princip...To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.展开更多
HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation.However,most of existing HVDC frequency con...HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation.However,most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency control.Since the traditional system frequency control is dominated by the thermal generators,the advantage of the fast response of the HVDC system is not made fully used.The development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC system.Different from other frequency deviation tracking strategies,a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load lost.In this paper,a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression(MRFR)algorithm is proposed.The simulation is carried out with an integrated power system model based on the North American interconnections.The simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event,while stabilizing the frequency in advance.展开更多
Structures of monotone systems and cold standby systems with exponen-tial life distributions and dependent components are studied. It is shown that a mono-tone system composed of components with multivariate HNBUE lif...Structures of monotone systems and cold standby systems with exponen-tial life distributions and dependent components are studied. It is shown that a mono-tone system composed of components with multivariate HNBUE life distributions isessentially a series system composed of components with multivariate exponential lifedistributions. Also, it is proved that for cold standby systems composed of componentswith multivariate NBU life distributions, all but oue of the components are degenerateat zero while the remaining one is exponential. In addition, several equivalent char-acterizations of multivariate exponential distribution are provided in the multivariateHNBUE life distribution class which include many existing results as special cases.展开更多
基金supported by the National Natural Science Foundation of China(71401052)the Key Project of National Social Science Fund of China(12AZD108)+2 种基金the Doctoral Fund of Ministry of Education(20120094120024)the Philosophy and Social Science Fund of Jiangsu Province Universities(2013SJD630073)the Central University Basic Service Project Fee of Hohai University(2011B09914)
文摘To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.
基金supported primarily by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under NSF Award Number EEC-1041877 and the CURENT Industry Partnership Program.
文摘HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation.However,most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency control.Since the traditional system frequency control is dominated by the thermal generators,the advantage of the fast response of the HVDC system is not made fully used.The development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC system.Different from other frequency deviation tracking strategies,a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load lost.In this paper,a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression(MRFR)algorithm is proposed.The simulation is carried out with an integrated power system model based on the North American interconnections.The simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event,while stabilizing the frequency in advance.
基金This work is supported by the Natural Science Foundation of the Jiangsu Provincial Education Commission.
文摘Structures of monotone systems and cold standby systems with exponen-tial life distributions and dependent components are studied. It is shown that a mono-tone system composed of components with multivariate HNBUE life distributions isessentially a series system composed of components with multivariate exponential lifedistributions. Also, it is proved that for cold standby systems composed of componentswith multivariate NBU life distributions, all but oue of the components are degenerateat zero while the remaining one is exponential. In addition, several equivalent char-acterizations of multivariate exponential distribution are provided in the multivariateHNBUE life distribution class which include many existing results as special cases.
