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In renewable energy generation systems, the DC-DC converter with high voltage gain is indispensable, which is used to convert the low output voltage of photovoltaic (PV) cells and fuel cells to the high DC bus voltage. In this study, a costeffective clamping capacitor boost (CCB) converter with high voltage gain is proposed. In the basis of the conventional boost converter, a clamping capacitor cell with two capacitors and two diodes is embedded. Then, the voltage gain of the proposed CCB converter can be doubled compared with the conventional boost converter. Further, unlike the switched capacitor high voltage gain converter, there is no large current spikes on the capacitors in the proposed CCB converter, which can reduce the current stresses and the costs of capacitors a lot. The operation principles and characteristics of the proposed CCB converter are analysed in detail and verified by simulation. Finally, a 450 W prototype is implemented to validate the performances and advantages of the proposed CCB converter. The proposed CCB converter provides a cost-effective choice for high voltage gain converters in the renewable energy generation system with PV cells.

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In this paper we develop a novel framework for inferring a generative model of network structure representing the causal relations between data for a set of objects characterized in terms of time series. To do this we make use of transfer entropy as a means of inferring directed information transfer between the time-series data. Transfer entropy allows us to infer directed edges representing the causal relations between pairs of time series, and has thus been used to infer directed graph representations of causal networks for time-series data. We use the expectation maximization algorithm to learn a generative model which captures variations in the causal network over time. We conduct experiments on fMRI brain connectivity data for subjects in different stages of the development of Alzheimer's disease (AD). Here we use the technique to learn class exemplars for different stages in the development of the disease, together with a normal control class, and demonstrate its utility in both graph multi-class and binary classifications. These experiments are showing the effectiveness of our proposed framework when the amounts of training data are relatively small.

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