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[Thesis] Thermal and EMI Modeling and Analysis of a Boost PFC Circuit Designed Using a Genetic-based Optimization Algorithm.pdf

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[Thesis] Thermal and EMI Modeling and Analysis of a Boost PFC Circuit Designed Using a Genetic-based Optimization Algorithm.pdf

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[Thesis] Thermal and EMI Modeling and Analysis of a Boost PFC Circuit Designed Using a Genetic-based Optimization Algorithm.pdf

文档介绍

文档介绍:Thermal and EMI Modeling and Analysis of a Boost
PFC Circuit Designed Using a ic-based
Optimization Algorithm

Erik Hertz

Thesis submitted to the faculty of
Virginia Polytechnic Institute and State University
in partial fulfillment of the requirements for the degree of
Master of Science
in
Electrical Engineering

Dr. Dushan Boroyevich, Chair
Dr. Dan Chen
Dr. Jason Lai

July 24, 2001
Blacksburg, VA

Keywords: optimization, ic algorithms, power factor correction (PFC),
ic interference (EMI), temperature rise, per unit analysis
Copyright 2001, Erik Hertz
Thermal and EMI Modeling and Analysis of a Boost
PFC Circuit Designed Using a ic-based
Optimization Algorithm

Erik Hertz

(ABSTRACT)

The boost power factor correction (PFC) circuit is mon circuit in power electronics.
Through years of experience, many designers have optimized the design of these circuits for
particular applications. In this study, a new design procedure is presented that guarantees
optimal results for any application. The algorithm used incorporates the principles of evolution
in order to find the best design. This new design technique requires a rethinking of the
traditional design process. Electrical models have been developed specifically for use with the
optimization tool. One of the main focuses of this work is the implementation and verification of
computationally efficient thermal and electro-ic interference (EMI) models for the boost
PFC circuit. The EMI model presented can accurately predict noise levels into the 100’s of
kilohertz range. The thermal models presented provide very fast predictions and they have been
adjusted to account for different thermal flows within the layout. This tuning procedure results
in thermal predictions within 10% of actual measurement data. In order to further reduce the
amount of analysis that the optimization tool must perform, some of the co