In order to enhance the efficiency and reliability for distributed microgeneration, a modular grid-connected photovoltaic (PV) generation system is proposed. It consists of modular dc-dc converters and modular dc-ac inverters. The outputs of dcdc converter and the inputs of dc-ac inverter share a dc bus. AC current coupling between the parallel-operated inverters, which is the key issue in this generation system, has been investigated. A current-decoupling method is proposed and implemented by regulating the currents of split-filter inductors, respectively. An optimal control strategy for the efficiency enhancement of PV generation system is proposed by utilizing the dispersion of control parameters. During power generation, only one modular dc-ac inverter is operating with nonfull load, and the other modular dc-ac inverters are operating with full load or at standby mode. A prototype of this modularized grid-connected PV generation system is implemented. The steady state and dynamic experimental results show that the fundamental components of two split-filter inductor currents in one inverter module are equivalent, which are decoupled completely, and only one inverter module operates with nonfull load among the parallel-operated inverter modules. The feasibility of the proposed system and the effectiveness of the control strategies have been verified by experimental results.

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We present a fast and robust object tracking algorithm by using 2DPCA andl2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt thel2-regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms.