Artificial Neural Network Models for Estimation of Electric Field Intensity and Magnetic Flux Density in the Proximity of Overhead Transmission Line

Turajlić, Emir; Alihodžić, Ajdin; Mujezinović, Adnan

doi:10.1093/rpd/ncac229

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Recently, ANNs [26][27][28] and genetic algorithm [29] have been applied for the overhead lines' electric and magnetic field determination. Among the different types of ANNs used for this purpose, the Multi-Layer Perceptron (MLP) type ANN stood out as the most effective and most commonly used [30][31][32]. This paper considers a method that utilizes ANN for magnetic flux density estimation in the vicinity of overhead lines.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network-based method for overhead lines magnetic flux density estimation

Alihodžić,

Mujezinović,

Turajlić

2024

Journal of Electrical Engineering

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This paper presents an artificial neural network (ANN) based method for overhead lines magnetic flux density estimation. The considered method enables magnetic flux density estimation for arbitrary configurations and load conditions for single-circuit, multi-circuit, and also overhead lines that share a common corridor. The presented method is based on the ANN model that has been developed using the training dataset that is produced by a specifically designed algorithm. This paper aims to demonstrate a systematic and comprehensive ANN-based method for simple and effective overhead lines magnetic flux density estimation. The presented method is extensively validated by utilizing experimental field measurements as well as the most commonly used calculation method (Biot - Savart law based method). In order to facilitate extensive validation of the considered method, numerous magnetic flux density measurements are conducted in the vicinity of different overhead line configurations. The validation results demonstrate that the used method provides satisfactory results. Thus, it could be reliably used for new overhead lines’ design optimization, as well as for legally prescribed magnetic flux density level evaluation for existing overhead lines.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network-based method for overhead lines magnetic flux density estimation

Alihodžić,

Mujezinović,

Turajlić

2024

Journal of Electrical Engineering

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“…For the electric model, RMSE and R-squared were 0.6172 and 0.9121, while for the magnetic field, they were 0.3602 and 0.9471, respectively. However, since these papers used analytical models with some simplifications, the final model might not be accurate enough for real-world study cases [36].…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines

Alipour Bonab,

Song,

Yazdani-Asrami

2023

Applied Sciences

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The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively.

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Electric Field Calculation and Comparative Analysis of Conventional and Unconventional Transmission Lines

Arafat,

Porkar,

Ghassemi

2024

IEEE Access

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Artificial Neural Network Models for Estimation of Electric Field Intensity and Magnetic Flux Density in the Proximity of Overhead Transmission Line

Cited by 5 publications

References 20 publications

Artificial neural network-based method for overhead lines magnetic flux density estimation

Artificial neural network-based method for overhead lines magnetic flux density estimation

A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines

Electric Field Calculation and Comparative Analysis of Conventional and Unconventional Transmission Lines

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