A Novel Method for Predicting Crosstalk in Lossy Twisted Pair Cable Based on Beetle Antennae Search and Implicit Wendroff FDTD Algorithm

Zhou, Jianming; Li, Shijin; Yan, Weili; Ji, Yanxing; Meng, Ziyang; Zhao, Yongtao; Liu, Xingfa

doi:10.1080/02726343.2021.1903215

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Then the current optimal solution is updated. Through this approach, the BAS can continuously search for better solutions, thereby finding the optimal solution on a global scale [31][32][33][34][35][36]. The search principle and abstract model of the longhorn beetle are shown in Figure 3.…”

Section: Construction Of Bas Algorithm For Distribution Network Optim...mentioning

confidence: 99%

Multi-Objective Dynamic Reconstruction of Distributed Energy Distribution Networks Based on Stochastic Probability Models and Optimized Beetle Antennae Search

Yan,

Luo,

et al. 2024

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In the dynamic optimization problem of the distribution network, a dynamic reconstruction method based on a stochastic probability model and optimized beetle antennae search is proposed. By implementing dynamic reconstruction of distributed energy distribution networks, the dynamic regulation and optimization capabilities of the distribution network can be improved. In this study, a random probability model is used to describe the uncertainty in the power grid. The beetle antennae search is used for dynamic multi-objective optimization. The performance of the beetle antennae search is improved by combining it with the simulated annealing algorithm. According to the results, the optimization success rate of the model was 98.7%. Compared with the discrete binary particle swarm optimization algorithm and bacterial foraging optimization algorithm, it was 9.3% and 26.1% faster, respectively. For practical applications, this model could effectively reduce power grid transmission losses, with a reduction range of 16.7–18.6%. Meanwhile, the charging and discharging loads were effectively reduced, with a reduction range of 16.2–19.7%. Therefore, this method has significant optimization effects on actual power grid operation. This research achievement contributes to the further development of dynamic reconstruction technology for distribution networks, improving the operational efficiency and stability of the power grid. This has important practical significance for achieving green and intelligent operation of the power system.

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