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Broad-scale modeling and optimization play a vital role in the design of advanced power converters. Optimization is normally implemented via brute force iterations of design variables or utilizing metaheuristic techniques which are time consuming for a wide range of potential topologies, device implementations, and operating points. Recently, discrete time state-space modeling has shown merits in rapid analysis and generality to arbitrary circuit topologies but has not yet been utilized under rapid optimization techniques across multiple converter parameters. In this work, we investigate methods to incorporate rapid gradient-based optimization techniques to leverage discrete time state-space modeling and showcase the approach in the power converter design process. The method is validated on a 48-to-1V converter designed using the proposed techniques.
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A Framework for Online Estimation of High Frequency Converter Circuit Parameters
ABSTRACT In this paper a parameter estimation method of high frequency switching power converters is proposed. Parameters are estimated through measurement of basic circuit voltage and current quantities and using simple feed forward neural networks to establish correlations between circuit parameter variations and general converter performance. This allows the estimation of internal semiconductor device or passive component parameters that would be challenging to measure directly. This approach serves as a promising enabler for power converter digital twins and for converter health monitoring. The proposed framework is developed and verified for an LLC resonant converter. Parameter predictions achieved mean absolute errors below 4.12% and an average MAE of 1.57% for all parameters.
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- Award ID(s):
- 2228873
- PAR ID:
- 10656347
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- Electronics Letters
- Volume:
- 61
- Issue:
- 1
- ISSN:
- 0013-5194
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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