In high-power-density power electronics applications, it is important to predict the power losses of semiconductor devices in order to maximize global system efficiency and avoid thermal damages of the components. When different effects influence the power losses, some of which difficult to be physically modeled, it is worthwhile to use empirical laws obtained starting from experimental data, like the Steinmetz's equation widely used for inductors' magnetic core losses prediction. This paper discusses a method to find empirical power loss models by using Genetic Programming (GP). In particular, the GP approach has been applied to identify power losses in Insulated Gate Bipolar Transistors for Induction Cooking application. A loss model has been obtained using an experimental training set, and the result has been successively validated.

In-system IGBT power loss behavioral modeling

FEMIA, Nicola;MIGLIARO, MARIO;TOLEDO, DAVIDE
2016-01-01

Abstract

In high-power-density power electronics applications, it is important to predict the power losses of semiconductor devices in order to maximize global system efficiency and avoid thermal damages of the components. When different effects influence the power losses, some of which difficult to be physically modeled, it is worthwhile to use empirical laws obtained starting from experimental data, like the Steinmetz's equation widely used for inductors' magnetic core losses prediction. This paper discusses a method to find empirical power loss models by using Genetic Programming (GP). In particular, the GP approach has been applied to identify power losses in Insulated Gate Bipolar Transistors for Induction Cooking application. A loss model has been obtained using an experimental training set, and the result has been successively validated.
2016
978-1-5090-0490-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4678559
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