Modeling plays a vital role in the design of advanced
power converters. Commonly, modeling is completed using either
dedicated hand analysis, which must be completed individually
for each topology, or time-stepping circuit simulations, which
are insufficiently rapid for broad analysis considering a wide
range of potential designs or operating points. Discrete time
state-space modeling of switching converters has shown merits
in rapid analysis and generality to arbitrary circuit topologies
but is hampered by difficulty incorporating nonlinear elements.
In this work, we investigate methods for the incorporation of
nonlinear elements into a generalized discrete time state-space
modeling framework and showcase the utility of the approach
for use in the converter design process.
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Steady-State Convergence of Discrete Time State-Space Modeling with State-Dependent Switching
Steady-state modeling plays an important role in the design of advanced power converters. Typically, steady-state modeling is completed by time-stepping simulators, which may be slow to converge to steady-state, or by dedicated analysis, which is time-consuming to develop across multiple topologies. Discrete time state-space modeling is a uniform approach to rapidly simulate arbitrary power converter designs. However, the approach requires modification to capture state-dependent switching, such as diode switching or current programmed modulation. This work provides a framework to identify and correct state-dependent switching within discrete time state-space modeling and shows the utility of the proposed method within the power converter design process.
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- Award ID(s):
- 1751878
- NSF-PAR ID:
- 10211924
- Date Published:
- Journal Name:
- 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics (COMPEL)
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/COMPEL49091.2020.9265734
