A THERMAL MODEL FOR IGBT MODULES AND ITS IMPLEMENTATION IN A REAL TIME SIMULATOR

As the power density and switching frequency increase, thermal analysis of power electronics system becomes imperative. The analysis provides valuable information on the semiconductor rating, long-term reliability and efficient heat-sink design. The aim of this thesis is to build a comprehensive thermal model for the power IGBT modules used in three-phase inverters in order to predict the dynamic junction temperature rise under real operating conditions. The thermal model is developed in two steps: first, the losses are calculated and then the junction temperature is estimated. The real-time simulation environment dictates the requirements for the models: easy implementation on the software platform SIMULINK and fast calculation time. The power losses model, which is based on the look-up table method for calculating the conduction and switching losses, are successfully built and implemented in the real time simulation environment. The power losses equations are derived from the experimental data. Several algorithms are developed to catch every switching event and to solve the synchronization problem in a real-time system. An equivalent RC network model is built to perform the thermal analysis. The parameters of the thermal network are extracted from the junction to case and case to ambient dynamic thermal impedance curves. Two separate approaches are followed in deriving these thermal characteristic curves. The first approach uses the experimental data available while the second approach uses the commercial software package ANSYS. The 3-D simulation results using ANSYS agree well with the experimental data and therefore can be relied upon to extract the parameters of the thermal RC network. The latter is successfully implemented using the transfer function method, and is then built in SIMULINK environment for the use in a real time simulator. The power losses and thermal RC network models are extensively tested in a real time simulator environment. The accuracy of the models is confirmed by comparing their predictions with the experimental data