A New Method for Twisted Wire Crosstalk Estimation Based on GA-BP Neural Network Algorithm

Authors

  • Wu Zhang Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China
  • Yongji Wu Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China
  • Jiafei Ding Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China
  • Yang Zhao Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China
  • Mingyuan He Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China

DOI:

https://doi.org/10.13052/2022.ACES.J.370601

Keywords:

Back propagation neural network(BPNN) algorithm, Genetic algorithm (GA), Multi-conductor transmission line (MTL), Crosstalk

Abstract

Based on the research of genetic algorithm (GA) to optimize the BP neural network algorithm, this paper proposes a method for predicting twisted wire crosstalk based on the algorithm. Firstly, the equivalent circuit model of a multi-conductor transmission line is established, combined with the method of similarity transformation, the second-order differential transmission line equations are decoupled into n groups of independent two-conductor transmission line equations, and the crosstalk is finally solved. Then the mathematical model of the twisted wire is established and its structural characteristics are analyzed, and the GA-BP neural network algorithm is used to realize the mapping of the electromagnetic parameter matrix of the twisted wire and the position of the twisted wire. Finally, the mapping relationship is substituted into the transmission line equation, and the near-end crosstalk (NEXT) and the far-end crosstalk (FEXT) of an example three-core twisted wire are predicted based on the idea of cascade combined. By comparing with the transmission line matrix method (TLM), it can be seen that the method proposed in this paper is in good agreement with the crosstalk results obtained by the electromagnetic field numerical method, which verifies the effectiveness of the algorithm proposed in this paper.

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Author Biographies

Wu Zhang, Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China

Wu Zhang was born in Anhui Province, China. He received his B.S degree from the School of Electrical Engineering and Automation from Xi’an University of Technology, Xi’an, China, in 2020. He is currently working toward a master’s degree in electrical engineering at Nanjing Normal University, Nanjing, China. His main research interests include multi-conductor transmission lines and EMC.

Yongji Wu, Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China

Yongji Wu was born in Shanxi Province, China. He received a bachelor’s degree from Shanxi Institute of Engineering and Technology in 2018 and is currently studying for a master’s degree in electrical engineering at Nanjing Normal University. The main research direction is the electromagnetic compatibility of the secondary equipment of the substation and the power system.

Jiafei Ding, Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China

Jiafei Ding was born in Nantong City, Jiangsu Province in 1996. He graduated from Nanjing Normal University with a bachelor’s degree in 2019 and is currently studying for a master’s degree at the School of Electrical and Automation Engineering of Nanjing Normal University. His main research interests are signal integrity and electromagnetic compatibility of power systems.

Yang Zhao, Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China

Yang Zhao received his B.E., M.E., and Ph.D. degree all in power electronic technology from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 1989 and 1992, and 1995, respectively. He is currently the professor with Nanjing Normal University. His research interests are in the areas of Electromagnetic Compatibility, Power Electronics and Automotive Electronics.

Mingyuan He, Department of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210046, China

Mingyuan He was born in Gansu Province, China. He graduated from Hohai University in 2017 with a bachelor’s degree. Currently studying for a master’s degree in electrical engineering at Nanjing Normal University. The main research directions are new energy station control, power system optimization operation and stability control.

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Published

2022-12-14

How to Cite

[1]
W. . Zhang, Y. . Wu, J. . Ding, Y. . Zhao, and M. . He, “A New Method for Twisted Wire Crosstalk Estimation Based on GA-BP Neural Network Algorithm”, ACES Journal, vol. 37, no. 06, pp. 655–663, Dec. 2022.

Issue

2022: Vol 37 Iss 6

Section

Articles