Thermal Design of Power Electronic Circuits

The heart of every switched mode converter consists of several switching semiconductor elements. Due to their non-ideal behaviour there are ON state and switching losses heating up the silicon chip. That heat must effectively be transferred to the environment in order to prevent overheating or even destruction of the element. For a cost-effective design, the semiconductors should be operated close to their thermal limits. Unfortunately the chip temperature cannot be measured directly. Therefore a detailed understanding of how losses arise, including their quantitative estimation, is required. Furthermore, the heat paths to the environment must be understood in detail. This paper describes the main issues of loss generation and its transfer to the environment and how it can be estimated by the help of datasheets and/or experiments.Comment: 17 pages, contribution to the 2014 CAS - CERN Accelerator School: Power Converters, Baden, Switzerland, 7-14 May 201

Thermal coupling analysis for a multi-chip paralleled IGBT module in a doubly fed wind turbine power converter

Thermal coupling between adjacent IGBT or diode chips is the result of non-uniform temperature distribution in a multi-chip IGBT module. This affects the junction temperatures and hence the total power loss predicted for the module. The study first investigates the impact of thermal coupling effect on the junction temperatures through finite element method (FEM), and then develops a thermal coupling impedance model to represent such effect. The effect is shown to reduce with the distance exponentially. The model result agrees well with test. The validated model is then used to predict the junction temperature swings during operational power cycling in a DFIG wind turbine, showing the difference between the rotor and grid side converters. The model presented and the results obtained may be important for reliability evaluation and condition monitoring in the wind turbine power converters as well as in other multi-chip paralleled power electronic systems

Electro-thermal modelling of multi chip power modules for high power converter application

In a compact power electronics systems such as converters, thermal interaction between components is inevitable. Traditional RC lumped modelling method does not take that into account and this would cause inaccuracy in the predicted temperature in the components of the systems. In this work, numerical simulation have been used to obtain detailed temperature distribution in power devices and the parameters for a Foster network behavior thermal model are extracted so that the thermal interaction can be accounted for and the model can be used to predict temperatures at all critical layers of the components. An ad-hoc conventional three-phase voltage source inverter (DC to AC converter) with a rating of 7.8 KW has been studied in this work as an example of the application of the proposed framework. The key component in the converter is a 75A11200V rated IGBT module. A power electronics circuit simulator is used to predict the power losses in the IGBT module and a Finite Element Analysis software is used to obtain the transient temperature profile in the module and the behaviour thermal model parameters are extracted using curve-fit approach. The resulting combined electro-thermal model is analysed using the circuit simulator again to obtain the temperature for various loading conditions. The results show that the proposed method can significantly improve the accuracy of predicted temperatures in the IGBT modules

An analysis of the thermal interaction between components in power converter applications

Accurately predicting the temperature of semiconductor devices is very important in the initial design of power electronics converter. RC thermal models derived from well-known methods have some ability to predict the temperature. However, the accuracy is boundary condition specific, hence, these methods cannot be used in the reliability analysis. To make the thermal model more accurate and robust the factors contributing to discrepancies need to be analyzed carefully. These are power-module-materials’ non-linear properties, thermal grease layer and the cooling system (i.e., liquid-cooled cold plate). In this work, estimation of accurate RC parameters from FEA thermal model is demonstrated in COMSOL. The electrical model having temperature dependent power loss model is coupled to refined thermal model and solved in a circuit simulator, PLECS. The proposed method is applied in two applications: assessing thermal interaction between IGBTs and anti-parallel diodes in a half-bridge power module, and assessing thermal interaction among the discrete switches in an interleaved bidirectional DC-DC converter. Results show that the impact of material non-linearity, thermal grease layer and cooling boundary conditions are significant for accurate prediction of IGBT and diode temperatures. The proposed model is consistent to FEA results and differs by 2-6.5% comparing to the experimental results

Health Condition Assessment of Multi-Chip IGBT Module with Magnetic Flux Density

To achieve efficient conversion and flexible control of electronic energy, insulated gate bipolar transistor (IGBT) power modules as the dominant power semiconductor devices are increasingly applied in many areas such as electric drives, hybrid electric vehicles, railways, and renewable energy systems. It is known that IGBTs are the most vulnerable components in power converter systems. To achieve high power density and high current capability, several IGBT chips are connected in parallel as a multi-chip IGBT module, which makes the power modules less reliable due to a more complex structure. The lowered reliability of IGBT modules will not only cause safety problems but also increase operation costs due to the failure of IGBT modules. Therefore, the reliability of IGBTs is important for the overall system, especially in high power applications. To improve the reliability of IGBT modules, this thesis proposes a new health state assessment model with a more sensitive precursor parameter for multi-chip IGBT module that allows for condition-based maintenance and replacement prior to complete failure. Accurate health condition monitoring depends on the knowledge of failure mechanism and the selection of highly sensitive failure precursor. IGBT modules normally wear out and fail due to thermal cycling and operating environment. To enhance the understanding of the failure mechanism and the external characteristic performance of multi-chip IGBT modules, an electro-thermal finite element model (FEM) of a multi-chip IGBT module used in wind turbine converter systems was established with considerations for temperature dependence of material property, the thermal coupling effect between components, and the heat transfer process. The electro-thermal FEM accurately performed temperature distribution and the distribution electrical characteristic parameters during chip solder degradation. This study found an increased junction temperature, large change of temperature distribution, and more serious imbalanced current sharing during a single chip solder aging, thereby accelerating the aging of the whole IGBT module. According to the change of thermal and electrical parameters with chip solder fatigue, the sensitivity of fatigue sensitive parameters (FSPs) was analyzed. The collector current of the aging chip showed the highest sensitivity with the chip solder degradation compared with the junction temperature, case temperature, and collector-emitter voltage. However, the current distribution of internal components remains inaccessible through direct measurements or visual inspection due to the package. As the relationship between the current and magnetic field has been studied and gradually applied in sensor technologies, magnetic flux density was proposed instead of collector current as a new precursor for health condition monitoring. Magnetic flux density distribution was extracted by an electro-thermal-magnetic FEM of the multi-chip IGBT module based on electromagnetic theory. Simulation results showed that magnetic flux density had even higher sensitivity than collector current with chip solder degradation. In addition, the magnetic flux density was only related with the current and was not influenced by temperature, which suggested good selectivity. Therefore, the magnetic flux density was selected as the precursor due to its better sensitivity, selectivity, and generality. Finally, a health state assessment model based on backpropagation neural network (BPNN) was established according to the selected precursor. To localize and evaluate chip solder degradation, the health state of the IGBT module was determined by the magnetic flux density for each chip and the corresponding operating conduction current. BPNN featured good self-learning, self-adapting, robustness and generalization ability to deal with the nonlinear relationship between the four inputs and health state. Experimental results showed that the proposed model was accurate and effective. The health status of the IGBT modules was effectively recognized with an overall recognition rate of 99.8%. Therefore, the health state assessment model built in this thesis can accurately evaluate current health state of the IGBT module and support condition-based maintenance of the IGBT module