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UBC Theses and Dissertations

Parametric average value modeling of flyback converters in ccm and dcm including parasitics and snubbers Sucu, Mehmet


Modeling of switched-mode DC-DC converters has been receiving significant interest due to their widespread applications. Averaged modeling is the most common approach (and tool) that has been used to analyze dynamic performance of converter circuits. Specifically, state-space averaged models are widely used because of their simplicity and generality. However, as has been shown in the literature, the challenges of directly applying this approach to predict the discontinuous variables (states) and include the parasitics and losses have limited application of this approach to a wider range of converter circuits. The recently introduced parametric average value models (PAVM) has a potential to overcome this problem. In this Thesis, first of all a second-order flyback converter has been investigated. An analytical solution of state-apace averaging and small-signal analysis of the flyback converter in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is given without and with parasitics. The PAVM methodology has been applied to the second-order model to overcome the problem of discontinuous state during the DCM. The snubber circuits in flyback converter have also been investigated. Appearance of snubbers in the model introduces a problem on the output voltage besides improving the efficiency prediction. It is shown that with the snubbers the conventional state-space averaging cannot predict the output voltage correctly in CCM and DCM. To solve this problem the model is partitioned into two different sub-circuits: i) switching sub-circuit circuit; and ii) non-switching sub-circuit. Thereafter it becomes possible apply the averaging on the switching sub-circuit only. Finally, a full-order flyback converter with two RC snubber circuits and all the basic parasitics is considered. The PAVM methodology has been extended to this class of switching converter for the first time. It is shown that including the snubbers and parasitics significantly improves the model accuracy in terms of predicting converter efficiency, which represents an appreciable improvement over all previously existing average models. The proposed model has been verified with detailed simulations and hardware measurements.

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