Reduction of Random Variables in EMC Uncertainty Simulation Model

Authors

  • Jinjun Bai Department of Electrical Engineering, Dalian Maritime University, Dalian, 116026, China
  • Yixuan Wan Department of Electrical Engineering, Dalian Maritime University, Dalian, 116026, China
  • Ming Li Aviation Industry Corporation of China (AVIC), AVIC Aero Polytechnol Estab, Beijing 100028, Peoples R China
  • Gang Zhang Harbin Inst Technol, Sch Elect Engn & Automat, Harbin Institute of Technology,Harbin 150001, Peoples R China
  • Xin He Harbin Inst Technol, Sch Elect Engn & Automat, Harbin Institute of Technology,Harbin 150001, Peoples R China

DOI:

https://doi.org/10.13052/2022.ACES.J.370903

Keywords:

Electromagnetic Compatibility, Uncertainty Analysis Method, Dimensional Disaster, Sensitivity Analysis Method, Random Variable

Abstract

To improve the reliability of simulation results, uncertainty analysis methods were developed in the Electromagnetic Compatibility (EMC) field. Random variables are used to describe random events. The more random variables you have, the less efficient the simulation is. Therefore, many high-accuracy methods have the problem of dimensional disaster, which means the calculation efficiency decreases exponentially with the increase of the number of random variables. A random variable reduction strategy based on sensitivity analysis method is proposed in this paper, so as to improve the computational efficiency of the global uncertainty analysis method.

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Author Biographies

Jinjun Bai, Department of Electrical Engineering, Dalian Maritime University, Dalian, 116026, China

Jinjun Bai received his B.Eng. degree in Electrical Engineering and Automation in 2013, and a Ph.D. degree in electrical engineering in 2019 from the Harbin Institute of Technology, Harbin, China.

He is now a lecturer at Dalian Maritime University. His research interests include uncertainty analysis methods in EMC simulation, EMC problem of electric vehicles, and the validation of CEM.

Yixuan Wan, Department of Electrical Engineering, Dalian Maritime University, Dalian, 116026, China

Yixuan Wan is working toward a Master’s Degree in Electrical Engineering. Her current research on simulation of electromagnetic radiation is related to electric vehicles. She is now engaged in electric vehicle cable harness crosstalk simulation.

Ming Li, Aviation Industry Corporation of China (AVIC), AVIC Aero Polytechnol Estab, Beijing 100028, Peoples R China

Ming Li was born in Tai’an, China, in 1982. He received B.Sc. and Ph.D.degrees from Beihang University, Beijng, China, in 2004 and 2010, respectively. He is currently a senior engineer at AVIC Aero Polytechnology Establishment, Beijing, China. His research interests include equipment environmental effects analysis and simulation.

Gang Zhang, Harbin Inst Technol, Sch Elect Engn & Automat, Harbin Institute of Technology,Harbin 150001, Peoples R China

Gang Zhang was born in Tai’an, China, in 1984. He received a B.Sc. in Electrical Engineering from China University of Petroleum, Dongying, China, in 2007, and M.Sc. and Ph.D. degrees in Electrical Engineering from Harbin Institute of Technology (HIT), Harbin, China, in 2009 and 2014, respectively.

He is currently an Associate Professor in electrical engineering at Harbin Institute of Technology, Harbin, China, and a Visiting Professor at University of L’Aquila, L’Aquila, Italy. His research interests include electrical contact theory, uncertainty analysis of electromagnetic compatibility, and the validation of CEM.

Xin He, Harbin Inst Technol, Sch Elect Engn & Automat, Harbin Institute of Technology,Harbin 150001, Peoples R China

Xin Hex was born in Wenshan, China, in 1996. He received B.S. and M.Sc. degrees in Electrical Engineering from Harbin Institute of Technology, China, in 2019 and 2021, respectively, where he is currently pursuing a Ph.D. degree. His research interests include cable fault detection and location, and finite element simulation.

References

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Published

2023-02-10

How to Cite

[1]
J. . Bai, Y. . Wan, M. . Li, G. . Zhang, and X. . He, “Reduction of Random Variables in EMC Uncertainty Simulation Model”, ACES Journal, vol. 37, no. 09, pp. 941–947, Feb. 2023.

Issue

2022: Vol 37 Iss 9

Section

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