Modeling Research On Power Module IGBT Switching Loss And Switching Time In The Process Of Dynamic

The switching loss of the power module IGBT will produce a certain quantity of heat,which causes a larger temperature rise.The extension of the switching time also causes the device to suffer some damage,it will seriously affect the safe and reliable operation of the device,but how to obtain accurate switching loss and switching time quickly and conveniently needs to be solved.This paper takes the power module IGBT as the research object,the research on the modeling of switching loss and switching time in the dynamic process is studied.The main works are as follows:(1)Firstly,the development process of power module IGBT and the research significance of its dynamic characteristics are discussed in detail.The dynamic characteristic testing methods and the switching loss calculation models of IGBT at home and abroad are summarized,which lays the theoretical foundation for the modeling of the switching loss and switching time during the dynamic process of the power module IGBT.Then,based on the detailed analysis of IGBT structure and operation characteristics,the dynamic characteristics of the power module IGBT are tested.The switching dynamic waveform and switching time under different test conditions are obtained,which provides the data base for modeling the switching loss and switching time.(2)In order to determine the input variables of the switching loss and switching time prediction model,the influence of various parameters on switching loss and switching time are analyzed.Based on the analysis,the IGBT switching loss and switching time prediction model based on the support vector machine optimized by the particle swarm optimization was established.Through the analysis of the prediction results,it is found that the optimization and prediction effect of this model are not ideal.Therefore,the traditional chicken swarm optimization is improved,the global search and local search capabilities of the whole algorithm were further balanced,so that flocks can achieve dynamic self-adjustment,the optimization performance of the whole algorithm is improved.The improved chicken swarm optimization was used to optimize the parameters of the support vector machine.Based on this,the IGBT switching loss and switching time prediction model based on support vector machine optimized by the improved chicken swarm optimization was established.(3)The switching loss and switching time of IGBT are predicted by using the support vector machine model optimized by the improved chicken swarm optimization,to further verify the accuracy and validity of this model,the true switching loss and switching time were compared respectively with the predicted results of the support vector machine modeloptimized by the improved chicken swarm optimization,the support vector machine model optimized by the chicken swarm optimization and the support vector machine model optimized by the particle swarm optimization.By comparing the convergence curves of the three models,it is found that the support vector machine model optimized by the improved chicken swarm optimization has the fastest convergence rate.The optimization effect is improved compared with the other two models.The relative error of the support vector machine model optimized by the improved chicken swarm optimization has a small overall fluctuation,and the prediction relative error is small,the prediction effect is the best of the three models.The average relative error of the support vector machine model optimized by the improved chicken swarm optimization is compared with that of the support vector machine model optimized by the chicken swarm optimization,the average relative error of switch-on loss,switch-off loss,switch-on time and switch-off time are reduced by 2.9344%,2.5446%,0.8029% and 0.6813%.In conclusion,the support vector machine model optimized by the improved chicken swarm optimization has important practical significance for improving the work efficiency of the device,improving the design structure of the radiator and realizing the life prediction of the power electronic device.