- author = "Nicola Femia and Mario Migliaro and Antonio {Della Cioppa}",
- title = "A Genetic Programming approach to modeling power losses of Insulate Gate Bipolar Transistors",
- booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)",
- year = "2016",
- editor = "Yew-Soon Ong",
- pages = "4705--4712",
- address = "Vancouver",
- month = "24-29 " # jul,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1-5090-0623-6",
-
DOI = "
doi:10.1109/CEC.2016.7744391",
-
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 behavioural 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.",
- notes = "WCCI2016",
