A comprehensive predictive strategy was proposed for the neutral-point balancing control of back-to-back three-level converters. The phase currents at both sides and the DC-link capacitor voltages were measured for th...A comprehensive predictive strategy was proposed for the neutral-point balancing control of back-to-back three-level converters. The phase currents at both sides and the DC-link capacitor voltages were measured for the prediction of the neutral-point current. A quality function was found to balance the neutral-point, and a metabolic on-times distribution factor was used as a predicator to minimize the quality function at each switching state. Simulation results show that the proposed method produces smaller ripples in tested signals compared with the established one, namely, 9.15% less in a total harmonic distortion(THD) of line-to-line voltage, 1.08% less in the THD of phase current, and 0.9 V less in the ripple of the neutral-point voltage. The obtained experimental results show that the main harmonics of the line-to-line voltage and the phase current in the proposed method are improved by 10 d B and 6 d B, respectively, and the ripple of neutral-point voltage is halved compared to the established one.展开更多
Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabele...Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.展开更多
基金Project(61074018)supported by the National Natural Science Foundation of ChinaProject(2012kfjj06)supported by Hunan Province Key Laboratory of Smart Grids Operation and Control(Changsha University of Science and Technology),China
文摘A comprehensive predictive strategy was proposed for the neutral-point balancing control of back-to-back three-level converters. The phase currents at both sides and the DC-link capacitor voltages were measured for the prediction of the neutral-point current. A quality function was found to balance the neutral-point, and a metabolic on-times distribution factor was used as a predicator to minimize the quality function at each switching state. Simulation results show that the proposed method produces smaller ripples in tested signals compared with the established one, namely, 9.15% less in a total harmonic distortion(THD) of line-to-line voltage, 1.08% less in the THD of phase current, and 0.9 V less in the ripple of the neutral-point voltage. The obtained experimental results show that the main harmonics of the line-to-line voltage and the phase current in the proposed method are improved by 10 d B and 6 d B, respectively, and the ripple of neutral-point voltage is halved compared to the established one.
基金Project (20121101004) supported by the Major Science and Technology Program of Shanxi Province,ChinaProject (20130321004-01) supported by the Key Technologies R&D Program of Shanxi Province,China+2 种基金Project (2013M530896) supported by the Postdoctoral Science Foundation of ChinaProject (2014021022-6) supported by the Shanxi Provincial Science Foundation for Youths,ChinaProject (80010302010053) supported by the Shanxi Characteristic Discipline Fund,China
文摘Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.
