In high-power-density power electronics application, it's important to be able to predict the power losses of semiconductor devices in order to maximize global system efficiency and to avoid thermal damages of the components. In this paper a novel approach to model the power losses of Insulate Gate Bipolar Transistors (IGBT) in Induction Cooking (IC) application is proposed. The inherent lack of precise physical IGBT loss model and the uncertainty of load in IC application has stimulated the idea to identify system-level behavioral power loss models that allow to cover a variety of devices and load conditions. For this goal, a Genetic Programming approach has been adopted, that starts from measured electrical quantities and returns a set of models, each one with the same structure but with different parameters relevant to the device under test. The models generated by the proposed method based on a training set of case studies have been merged into a generalized model and verified through a validation set.

A genetic programming approach to modeling power losses of Insulate Gate Bipolar Transistors

FEMIA, Nicola;MIGLIARO, MARIO;DELLA CIOPPA, Antonio
2016-01-01

Abstract

In high-power-density power electronics application, it's important to be able to predict the power losses of semiconductor devices in order to maximize global system efficiency and to avoid thermal damages of the components. In this paper a novel approach to model the power losses of Insulate Gate Bipolar Transistors (IGBT) in Induction Cooking (IC) application is proposed. The inherent lack of precise physical IGBT loss model and the uncertainty of load in IC application has stimulated the idea to identify system-level behavioral power loss models that allow to cover a variety of devices and load conditions. For this goal, a Genetic Programming approach has been adopted, that starts from measured electrical quantities and returns a set of models, each one with the same structure but with different parameters relevant to the device under test. The models generated by the proposed method based on a training set of case studies have been merged into a generalized model and verified through a validation set.
978-1-5090-0622-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4677728
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