A Genetic Programming approach to modeling power losses of Insulate Gate Bipolar Transistors

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@InProceedings{Femia:2016:CEC,
  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",
}

Genetic Programming entries for Nicola Femia Mario Migliaro Antonio Della Cioppa

Citations