Simulation of Pulse Width Modulation DC-DC Converters Through Symbolic Analysis Techniques

Piccirilli, Maria Cristina; Grasso, Francesco; Luchetta, Antonio; Manetti, S.; Reatti, Alberto

doi:10.25046/aj060132

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“…This means that these network functions do not have the classical meaning and then cannot be used for determining the frequency domain behavior of the whole circuit. If a traditional frequency domain simulation is required, it is necessary to replace the switch-diode cell with a time-invariant equivalent circuit [41][42][43][44][45][46][47] so that the circuit is linearized. However, the SapWinPE network functions can be used to determine the signature curves, as described above, within each of two or three possible working intervals of a converter, depending on the operating mode.…”

Section: Second Method: Graphical Evaluation Of Testabilitymentioning

confidence: 99%

Testability Evaluation in Time-Variant Circuits: A New Graphical Method

et al. 2022

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DC–DC converter fault diagnosis, executed via neural networks built by exploiting the information deriving from testability analysis, is the subject of this paper. The networks under consideration are complex valued neural networks (CVNNs), whose fundamental feature is the proper treatment of the phase and the information contained in it. In particular, a multilayer neural network based on multi-valued neurons (MLMVN) is considered. In order to effectively design the network, testability analysis is exploited. Two possible ways for executing this analysis on DC–DC converters are proposed, taking into account the single-fault hypothesis. The theoretical foundations and some applicative examples are presented. Computer programs, based on symbolic analysis techniques, are used for both the testability analysis and the neural network training phase. The obtained results are very satisfactory and demonstrate the optimal performances of the method.

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