Estimation of Thermal Network Models Parameters Based on Particle Swarm Optimization Algorithm

One of the biggest challenges in the Design for Reliability (DfR) methodology implementation into the development process are the extremely short delivery dates of Minimum Viable Product (MVP). This impediment is frequently encountered in the development of power supplies for plasma processing. In such case it is impossible to perform comprehensive but time-consuming simulations and the whole DfR process is focused on the stressors levels evaluation in working device. The goal for simulation stage is thus to choose most promising solution fulfilling specified requirements and rough estimation of chosen stressors levels. Time limitations and the demand on high quality already at the MVP stadium of the power supply development, raise a need to develop a simple and fast method for thermal modeling of critical components used in the designed power supply. In this paper, the particle swarm optimization (PSO) is introduced as an effective approach for the parameter estimation for thermal modelling of the power semiconductor modules used in power supplies for plasma processing.

[1]  Alexander Hensler,et al.  Thermal Impedance Monitoring during Power Cycling Tests , 2011 .

[2]  Mehdi Bigdeli,et al.  A Particale Swarm Optimization based method for estimation of transformer insulation model parameters , 2012, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[3]  Rik W. De Doncker,et al.  Monitoring 3-D Temperature Distributions and Device Losses in Power Electronic Modules , 2019, IEEE Transactions on Power Electronics.

[4]  J. Lutz,et al.  Power cycling reliability results of GaN HEMT devices , 2018, 2018 IEEE 30th International Symposium on Power Semiconductor Devices and ICs (ISPSD).

[5]  Jiabin Wang,et al.  Real-Time Measurement of Temperature Sensitive Electrical Parameters in SiC Power MOSFETs , 2018, IEEE Transactions on Industrial Electronics.

[6]  Rajeev Gupta,et al.  Controller parameter optimization using hybrid BFOA-PSO algorithm , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[7]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[8]  Robert D. Lorenz,et al.  Real-Time Junction Temperature Sensing for Silicon Carbide MOSFET With Different Gate Drive Topologies and Different Operating Conditions , 2018, IEEE Transactions on Power Electronics.

[9]  Frede Blaabjerg,et al.  A 3-D-Lumped Thermal Network Model for Long-Term Load Profiles Analysis in High-Power IGBT Modules , 2016, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[10]  Y. Bulut,et al.  Thermal modeling for power MOSFETs in DC/DC applications , 2004, 5th International Conference on Thermal and Mechanical Simulation and Experiments in Microelectronics and Microsystems, 2004. EuroSimE 2004. Proceedings of the.

[11]  Ke Ma,et al.  Junction Temperature Control for More Reliable Power Electronics , 2018, IEEE Transactions on Power Electronics.

[12]  Asantha Kempitiya,et al.  An electro-thermal performance analysis of SiC MOSFET vs Si IGBT and diode automotive traction inverters under various drive cycles , 2018, 2018 34th Thermal Measurement, Modeling & Management Symposium (SEMI-THERM).

[13]  Frede Blaabjerg,et al.  Complete Loss and Thermal Model of Power Semiconductors Including Device Rating Information , 2015, IEEE Transactions on Power Electronics.

[14]  Robert D. Lorenz,et al.  Evaluating different implementations of online junction temperature sensing for switching power semiconductors , 2015, 2015 IEEE Energy Conversion Congress and Exposition (ECCE).

[15]  Zhongwei Qi,et al.  Thermal management of MOSFET junction temperature in RF amplifier , 2011, 2011 27th Annual IEEE Semiconductor Thermal Measurement and Management Symposium.

[16]  Adel A. El-Samahy,et al.  A modified design of PID controller for DC motor drives using Particle Swarm Optimization PSO , 2009, 2009 International Conference on Power Engineering, Energy and Electrical Drives.

[18]  Kan Liu,et al.  Parameter Estimation for VSI-Fed PMSM Based on a Dynamic PSO With Learning Strategies , 2017, IEEE Transactions on Power Electronics.

[19]  Juan A. Lazzús,et al.  Neural network-particle swarm modeling to predict thermal properties , 2013, Math. Comput. Model..