Open AccessJunction Temperature Prediction of IGBT Power Module Based on BP Neural Network
Author(s) -
Junke Wu,
Luowei Zhou,
Xiong Du,
Pengju Sun
Publication year - 2014
Publication title -
journal of electrical engineering and technology/journal of electrical engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2093-7423
pISSN - 1975-0102
DOI - 10.5370/jeet.2014.9.3.970
Subject(s) - insulated gate bipolar transistor , junction temperature , artificial neural network , power (physics) , electrical engineering , power network , computer science , engineering , artificial intelligence , voltage , physics , electric power system , thermodynamics
In this paper, the artificial neural network is used to predict the junction temperature of the IGBT power module, by measuring the temperature sensitive electrical parameters (TSEP) of the module. An experiment circuit is built to measure saturation voltage drop and collector current under different temperature. In order to solve the nonlinear problem of TSEP approach as a junction temperature evaluation method, a Back Propagation (BP) neural network prediction model is established by using the Matlab. With the advantages of non-contact, high sensitivity, and without package open, the proposed method is also potentially promising for on-line junction temperature measurement. The Matlab simulation results show that BP neural network gives a more accuracy results, compared with the method of polynomial fitting.