Uncertainty Quantification of the Crosstalk in Multiconductor Transmission Lines via Degree Adaptive Stochastic Response Surface Method

Authors

  • Quanyi Yu College of Instrument Science and Electrical Engineering Jilin University, Changchun, 130026, China
  • Wei Liu College of Automotive Engineering Jilin University, Changchun, 130022, China
  • Kaiyu Yang College of Automotive Engineering Jilin University, Changchun, 130022, China
  • Xilai Ma Commercial Vehicle Development Institute Electric Department FAW JIEFANG, Changchun, 130011, China
  • Tianhao Wang College of Instrument Science and Electrical Engineering Jilin University, Changchun, 130026, China

Keywords:

Crosstalk, degree adaptive, multiconductor transmission lines (MTLs), statistical property, stochastic response surface method

Abstract

The degree adaptive stochastic response surface method is applied to analyze statistically the crosstalk in multiconductor transmission lines (MTLs). The coefficient of polynomial chaos expansion (PCE) is obtained based on the least angle regression. The truncation degree of PCE is iterated using the degree adaptive truncation algorithm, and the optimal proxy model of the crosstalk of the original MTLs that satisfies the actual error requirements is calculated. The statistical properties of crosstalk in MTLs (such as mean, standard deviation, skewness, kurtosis, and probability density distribution) are obtained. The failure probability of the electromagnetic compatibility in the MTLs system is considered. The global sensitivity indices of crosstalkrelated factors are analyzed. Finally, the proposed method is proved to be effective compared with the conventional Monte Carlo method. The uncertainty quantification of crosstalk in MTLs can be calculated efficiently and accurately.

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Published

2021-02-01

How to Cite

[1]
Quanyi Yu, Wei Liu, Kaiyu Yang, Xilai Ma, and Tianhao Wang, “Uncertainty Quantification of the Crosstalk in Multiconductor Transmission Lines via Degree Adaptive Stochastic Response Surface Method”, ACES Journal, vol. 36, no. 2, pp. 174–183, Feb. 2021.

Issue

2021: Vol 36 Iss 2

Section

General Submission