Abstract
In this paper, a new algorithm for predicting per unit length (p.u.l) parasitic parameters of transmission line is proposed. In the twisted-wire pair (TWP), different rotation degrees correspond to different parasitic parameters, which brings difficulties to the solution of telegraph equation. At present, the mainstream method is to divide TWP and use the cascade theory to solve each segment as a parallel transmission line. Therefore, we propose to use the beetle swarm optimization (BSO) algorithm to optimize the weights of the back propagation neural network, use a certain sample to train the network. This algorithm is used to predict the p.u.l parameters of uniform and non-uniform TWP, and the finite-difference time-domain (FDTD) method is used to solve telegraph equation. Among them, the crosstalk value of the uniform TWP is compared with the simulation value in the CST to verify the effectiveness of the proposed algorithm; the non-uniform TWP performs 500 random generation calculations to give the maximum envelope value of the crosstalk.
Highlights
With the increasing frequency and integration of current power equipment, the problem of crosstalk between transmission lines cannot be ignored [1], [2]
The equivalent cable bundle method (ECBM) is used with some modifications to predict crosstalk within a bundle of twisted-wire pair (TWP) [11]
This paper proposes to use the beetle swarm optimization (BSO) algorithm to optimize the weights of the back propagation (BP) neural network, that is, the input of the optimized algorithm is the weight and the output is the network error
Summary
With the increasing frequency and integration of current power equipment, the problem of crosstalk between transmission lines cannot be ignored [1], [2]. The processing method for TWP crosstalk is generally to divide it as a cascade of parallel transmission lines [7]. In [8], a new non-uniform TWP modeling method is proposed to simulate the change of the center position of the TWP. In the actual production process, the twisting distance of each segment will produce random fluctuations, which adds difficulty to the prediction of crosstalk, so we will first introduce a modeling method of non-uniform TWP [20].
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