A Statistical Approach to Electro-thermal Simulations with Contacts

All devices in the power grid have to withstand high short-time currents, that may occur as a consequence of short-circuits. The thermally critical areas during short-circuit testing of power devices are usually the electrical contacts between conductors. Connections and contacts with insufficient short-time current withstand capability may lead to local melting of the material and failure of the respective tests during the product development. When thermal performance is evaluated by simulations, the actual electric contact resistance is often unknown and its value is usually simply assumed or estimated. In this work, we consider the possibility of overcoming such uncertainty by making a step towards a statistical approach. We examine how simulating for a range of possible electric contact resistances can translate into a probability distribution of the maximum temperature and, hence, into an estimation of a likelihood of failure. The paper provides simulation results of industrial cases calculated and compared to experiments as well as example results of the proposed idea. The method will help optimize the use of conductor material, reduce the testing effort, and develop power devices with an increased short-circuit rating.

[1]  Stephan Schlegel,et al.  The impact of short circuits on contact elements in high power applications , 2017, 2017 IEEE Holm Conference on Electrical Contacts.

[2]  Sebastian Schöps,et al.  Modeling of Spatial Uncertainties in the Magnetic Reluctivity , 2016, ArXiv.

[3]  Ralf Hiptmair,et al.  Electro-Thermal Simulations with Skin-Layers and Contacts , 2018 .

[4]  S. Grossmann,et al.  The Influence of Peak Current and ECR on the Transmission Performance of High Power Connectors during Faults , 2018, 2018 IEEE Holm Conference on Electrical Contacts.

[5]  Tom Dhaene,et al.  Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits , 2018 .

[6]  Omar M. Knio,et al.  Spectral Methods for Uncertainty Quantification , 2010 .

[7]  Sebastian Schöps,et al.  Electrothermal simulation of bonding wire degradation under uncertain geometries , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[8]  H. Najm,et al.  Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection , 2003 .

[9]  Stéphane Clenet.  Uncertainty Quantification in Computational Electromagnetics : The stochastic approach , 2019 .

[10]  Gary R. Mirams,et al.  Uncertainty and variability in computational and mathematical models of cardiac physiology , 2016, The Journal of physiology.

[11]  Ragnar Holm,et al.  Electric contacts handbook , 1958 .

[12]  Habib N. Najm,et al.  Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics , 2009 .

[13]  M. Martinez Duro,et al.  Parameter uncertainty assessment in the calculation of the overvoltages due to transformer energization in resonant networks , 2012 .

[14]  Roland Platz,et al.  Uncertainty quantification for decision making in early design phase for passive and active vibration isolation , 2016 .

[15]  Jovica V. Milanovic,et al.  Quantification of Uncertainty in End-of-Life Failure Models of Power Transformers for Transmission Systems Reliability Studies , 2016, IEEE Transactions on Power Systems.