A Modeling Method for Switching Loss Prediction in igbt Dynamic Process

A technology of switching loss and modeling method, which is applied in the direction of calculation model, biological model, biological neural network model, etc., can solve the problems of low prediction accuracy, slow prediction speed, slow simulation speed, etc., and achieve strong expansion ability and high Reliable Prediction, Effect of Improving Reliability

Active Publication Date: 2022-04-12
HEBEI UNIV OF TECH
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Problems solved by technology

First of all, the switching loss calculation based on the physical model is to use the simulation software to simulate the dynamic characteristics of the IGBT, and then calculate the switching loss of the IGBT. The calculation result is more accurate, but the process of building the model is more complicated and the simulation speed is slower; the second is The switching loss calculation based on the mathematical model, the common method is to consult the IGBT technical manual to calculate the switching loss, but the calculated value is very different from the actual value, and although the polynomial model has improved the prediction accuracy, the prediction speed is relatively slow; finally, based on The switching loss prediction of the intelligent model has improved the prediction accuracy and prediction speed compared with the former two. However, in terms of parameter selection of the intelligent model, if the parameter selection is improper, the intelligent model will fall into the local optimal solution, which is not conducive to the model to find the global optimal solution. Excellent solution
[0005] Therefore, the prediction accuracy of existing measurement methods is not high, and the selection of optimal intelligent model parameters to predict IGBT switching loss and other issues

Method used

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  • A Modeling Method for Switching Loss Prediction in igbt Dynamic Process
  • A Modeling Method for Switching Loss Prediction in igbt Dynamic Process
  • A Modeling Method for Switching Loss Prediction in igbt Dynamic Process

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Embodiment

[0076] The present invention uses a PC as a platform for model building, wherein the CPU is i5-3230M 2.60GHz, the installed memory is 4GB, the operating system is Windows 7-64 bits, and MATLAB R2016a version is used. The IGBT module is MMG75S120B6HN from Macmic. The rated value of the module is 1200V / 75A. The module includes two identical IGBT chips and freewheeling diodes, and the distance between the IGBT chip and the FWD chip is 6.4 mm.

[0077] Step 1: Obtain IGBT dynamic characteristic test data

[0078] (1.1) Obtain 240 sets of test data through the IGBT dynamic characteristic test, each set of data includes DC bus voltage, collector current, gate voltage and switching frequency data, as well as the turn-on and turn-off loss data of the IGBT module;

[0079] Step 2: Normalize and distribute the IGBT dynamic characteristic test data

[0080] (2.1) Use formula (1) to normalize the IGBT characteristic test data;

[0081] (2.2) Divide the normalized test data into learning...

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Abstract

The invention relates to a modeling method of switching loss prediction in the dynamic process of IGBT, which is an IGBT switching loss prediction method based on the Jiadian krill swarm algorithm optimization extreme learning machine. The steps are: first, obtain the IGBT dynamic characteristic test data; secondly, After completing the experimental data processing, the basic parameter setting of the extreme learning machine and the krill swarm algorithm, use the good point set algorithm to optimize the initial krill group, use it as the weight threshold of the extreme learning machine, and calculate the good point krill fitness . In the optimization process, Jiadian krill constantly updates its position with Levy flight and cosine control factor as the wing, and calculates the fitness of Jiadian krill until the end; finally, according to the limit learning of Jiadian krill The optimal weight threshold of the machine is used to predict and output IGBT on and off loss values. The invention implements dynamic adjustment for algorithm optimization, so that the prediction model has high prediction accuracy and fast prediction speed, and the prediction result has good guiding significance for engineers to improve the heat dissipation system of the IGBT module and the like.

Description

technical field [0001] The technical scheme of the invention belongs to the technical field of IGBT reliability of power electronic devices, specifically a modeling method for switching loss prediction in the dynamic process of IGBT. Background technique [0002] With the continuous aggravation of the energy crisis, the continuous development of power electronic technology has effectively promoted the progress and development of society. IGBT, as a modern power electronic switch, is widely used in power systems, electric vehicles and high-speed traction and other fields. However, the faults of photovoltaic inverters with IGBT as the core account for about 37% of the total faults. Among the failures of power electronic systems, the failures caused by temperature account for about 55% of the total failures, and there is a negative correlation between the temperature of the device and its safety margin and thermal cycle life. The occurrence of faults seriously affects the nor...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02M1/08G06F30/367G06F30/27G06N3/04G06N3/00
CPCH02M1/08G06F30/367G06F30/27G06N3/04G06N3/006
Inventor 刘伯颖陈国龙胡佳程王海宇刘玉伟李玲玲
Owner HEBEI UNIV OF TECH
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