Uncertainty Quantification of Self-Breakdown Switches in a Circuit Model Using Wiener-Haar Expansion

Since many electromagnetic compatibility tests are resourcelimited, it is important to quantify the uncertainties of instruments beforehand. One of the common instruments is the trigger pulse generator with self-breakdown switches. Due to the abrupt changing caused by self-breakdown switches, stocha...

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Bibliographic Details
Published in:IEEE transactions on electromagnetic compatibility 2022-02, Vol.64 (1), p.27-32
Main Authors: Ge, Xiao-yu, Xie, Yan-zhao, Chen, Yu-hao, Liang, Tao
Format: Article
Language:English
Subjects:
Breakdown
Capacitors
Circuits
Computing time
Control systems
Electromagnetic compatibility
Generators
Haar wavelets
Integrated circuit modeling
Mathematical model
Monte Carlo simulation
Orthogonality
Polynomials
self-breakdown switch
statistical analysis
stochastic circuit simulation
Stochastic processes
Switches
Thermal expansion
Time lag
Trigger pulse generators
Uncertainty
uncertainty quantification (UQ)
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Summary:Since many electromagnetic compatibility tests are resourcelimited, it is important to quantify the uncertainties of instruments beforehand. One of the common instruments is the trigger pulse generator with self-breakdown switches. Due to the abrupt changing caused by self-breakdown switches, stochastic methods based on the generalized polynomial chaos suffer from Gibbs-type phenomena and become erroneous around abrupt transition point. This article proposes an effective technique based on the Haar wavelets for quantifying the time delay of self-breakdown switches in stochastic simulations. Wiener-Haar wavelet expansion method is suitable for dealing with abrupt-changing problems and is applied to the nodal-analysis-based circuit model. The stochastic circuit model is expanded to an augmented model with deterministic coefficients by exploiting wavelets' orthogonality. Furthermore, the distribution of time delay is represented as the mean value of a Heaviside function, which is implemented by an auxiliary circuit in the model. The statistical mean can be directly obtained from the 0th coefficient of Wiener-Haar expansion. A case study of a trigger generator for electromagnetic pulse generators is provided to validate the effectiveness of the proposed method. It is shown that, compared with Monte Carlo methods, Wiener-Haar representation provides robust results with less computational time and a faster convergence rate.
ISSN:0018-9375
1558-187X
DOI:10.1109/TEMC.2021.3087200