A Stochastic Modeling and Artificial Neural Network-Based Method for Electric and Magnetic Field Reduction Near Overhead Transmission Lines

被引：0

作者：

Alihodzic, Ajdin ^{[1
]}

Mujezinovic, Adnan ^{[1
]}

Turajlic, Emir ^{[1
]}

Dedovic, Maja Muftic ^{[1
]}

Dautbasic, Nedis ^{[1
]}

机构：

[1] Univ Sarajevo, Fac Elect Engn, Sarajevo, Bosnia & Herceg

来源：

2025 24TH INTERNATIONAL SYMPOSIUM INFOTEH-JAHORINA, INFOTEH | 2025年

关键词：

artificial neural networks; electric field; magnetic field; overhead transmission lines; reduction; stochastic modeling; MITIGATION;

D O I：

10.1109/INFOTEH64129.2025.10959217

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers the approach for overhead transmission lines' (OHTL) electric and magnetic field reduction by finding the best phase conductors (PCs) and shield wires (SWs) positions. This approach combines an algorithm based on stochastic modeling for OHTL configuration generation, with the artificial neural networks (ANN) based method for the electric field strength and magnetic flux density determination. This approach enables the generation of an arbitrary number of different OHTL configurations, taking into account specific user-defined limitations. This further, enables to find the OHTL designs that are the best solution for the considered case study regarding electric and magnetic field levels. In this paper, a case study is presented where the considered approach is employed to find the OHTL designs that give the best results regarding the limitation of electric and magnetic field values.

引用

页数：5

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