An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current(HVAC/HVDC) power system is presented to study power system stability. It is w...An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current(HVAC/HVDC) power system is presented to study power system stability. It is well known that appropriate coupling of inputs-outputs signals in the multivariable HVDC-HVAC system can improve the performance of designed supplemetary controller. In this work, different analysis techniques are used to measure controllability and observability of electromechanical oscillation mode. Also inputs–outputs interactions are considered and suggestions are drawn to select the best signal pair through the system inputs-outputs. In addition, a supplementary online adaptive controller for nonlinear HVDC to damp low frequency oscillations in a weakly connected system is proposed. The results obtained using MATLAB software show that the best output-input for damping controller design is rotor speed deviation as out put and phase angle of rectifier as in put. Also response of system equipped with adaptive damping controller based on HVDC system has appropriate performance when it is faced with faults and disturbance.展开更多
A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input d...A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.展开更多
A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2...A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2011-2013. By combining the principle of hierarchical decomposition into D&L method, a hierarchical decomposition model for multilevel SDA is obtained. The developed hierarchical IO-SDA model would provide consistent results and need less computation effort compared with the traditional SDA model. The decomposition results of the steel plant suggest that the technology improvement and reduced steel final demand are two major reasons for declined total energy consumption. The technical improvements of blast furnaces, basic oxygen furnaces, the power plant and the by-products utilization level have contributed mostly in reducing energy consumption. A major retrofit of ancillary process units and solving fuel substitution problem in the sinter plant and blast furnace are important for further energy saving. Besides the empirical results, this work also discussed that why and how hierarchical SDA can be applied in a process-level decomposition analysis of aggregated indicators.展开更多
文摘An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current(HVAC/HVDC) power system is presented to study power system stability. It is well known that appropriate coupling of inputs-outputs signals in the multivariable HVDC-HVAC system can improve the performance of designed supplemetary controller. In this work, different analysis techniques are used to measure controllability and observability of electromechanical oscillation mode. Also inputs–outputs interactions are considered and suggestions are drawn to select the best signal pair through the system inputs-outputs. In addition, a supplementary online adaptive controller for nonlinear HVDC to damp low frequency oscillations in a weakly connected system is proposed. The results obtained using MATLAB software show that the best output-input for damping controller design is rotor speed deviation as out put and phase angle of rectifier as in put. Also response of system equipped with adaptive damping controller based on HVDC system has appropriate performance when it is faced with faults and disturbance.
基金supported by Chiang Mai University Research Fund under the contract number T-M5744
文摘A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.
基金Project(2012GK2025)supported by Science-Technology Plan Foundation of Hunan Province,ChinaProject(2013zzts039)supported by the Fundamental Research Funds for Central South University,China
文摘A hierarchical structural decomposition analysis(SDA) model has been developed based on process-level input-output(I-O) tables to analyze the drivers of energy consumption changes in an integrated steel plant during 2011-2013. By combining the principle of hierarchical decomposition into D&L method, a hierarchical decomposition model for multilevel SDA is obtained. The developed hierarchical IO-SDA model would provide consistent results and need less computation effort compared with the traditional SDA model. The decomposition results of the steel plant suggest that the technology improvement and reduced steel final demand are two major reasons for declined total energy consumption. The technical improvements of blast furnaces, basic oxygen furnaces, the power plant and the by-products utilization level have contributed mostly in reducing energy consumption. A major retrofit of ancillary process units and solving fuel substitution problem in the sinter plant and blast furnace are important for further energy saving. Besides the empirical results, this work also discussed that why and how hierarchical SDA can be applied in a process-level decomposition analysis of aggregated indicators.
