In this paper, a novel method for electric field intensity and magnetic induction estimation in the vicinity of the high voltage overhead transmission lines is proposed. The proposed method is based on two fully connected feed-forward neural networks to independently estimate electric field intensity and magnetic induction. The artificial neural networks are trained using the scaled conjugate gradient algorithm. Training datasets corresponds to different overhead transmission line configurations that are generated using an algorithm that is especially developed for this purpose. The target values for the electric field intensity and magnetic induction datasets are calculated using the charge simulation method and Biot-Savart law based method, respectively. This data is generated for fixed applied voltage and current intensity values. In instances when the applied voltage and current intensity values differ from those used in the artificial neural network training, the electric field intensity and magnetic induction results are appropriately scaled. In order to verify the validity of the proposed method, a comparative analysis of the proposed method with the charge simulation method for electric field intensity calculation and Biot-Savart law-based method for magnetic induction calculation is presented. Furthermore, the results of the proposed method are compared to measurement results obtained in the vicinity of two 400 kV transmission lines. The performance analysis results showed that proposed method can produce accurate electric field intensity and magnetic induction estimation results for different overhead transmission line configurations. INDEX TERMSArtificial neural networks (ANN), Biot-Savart (BS) law based method, Charge simulation method (CSM), Electric field intensity, Magnetic induction, Scaled Conjugated Gradient (SCG)

In this paper, a novel method for the magnetic flux density estimation in the vicinity of multi-circuit overhead transmission lines is proposed. The proposed method is based on a fully connected feed-forward artificial neural network model that is trained to estimate the magnetic flux density vector components for a range of single-circuit overhead transmission lines. The proposed algorithm is able to simplify estimation process in instances when there are two or more geometrically identical circuits present in the multi-circuit overhead transmission line. In such instances, artificial neural network model is employed to estimate the magnetic flux density distribution over a considered lateral profile for only one of such circuits. The magnetic flux density estimates of the other geometrically identical circuits are derived from these results. The proposed methodology defines the resultant magnetic flux density for the multi-circuit overhead transmission line in terms of the contributions made by individual circuits. The application of the proposed magnetic flux density estimation method is demonstrated on several multicircuit configurations of overhead transmission lines. The performance of the proposed method is compared with the Biot-Savart law based method calculation results as well as with field measurement results. INDEX TERMSArtificial Neural Network (ANN), Biot-Savart (BS) law based method, Current intensity, Magnetic flux density, Multi-Circuit Overhead Transmission Lines.

This paper presents a new approach for uncertainty determination of the electric field intensity and magnetic flux density calculation in the vicinity of overhead transmission lines. The proposed method is based on the law of propagation of uncertainty as defined in the Guide to the expression of Uncertainty in Measurement. A mathematical model is developed for determining the electric field intensity and magnetic flux density calculation uncertainty based on the Charge simulation method and method based on Biot -Savart law, respectively. The verification of the proposed method was performed by estimating the uncertainty of the electric field and magnetic flux density calculations for four single circuit and two double circuit high-voltage overhead transmission lines. The analysis of the obtained results demonstrates that the proposed method can be successfully used to determine the uncertainty of electric field intensity and magnetic flux density calculations in the vicinity of overhead transmission lines.

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This paper considers the application of artificial neural network (ANN) models for electric field intensity and magnetic flux density estimation in the proximity of overhead transmission lines. Specifically, two distinct ANN models are used to facilitate independent estimation of electric field intensity and magnetic flux density in the proximity of overhead transmission lines. The considered ANN approach is systematically evaluated under different scenarios. An example of an overhead transmission line with horizontal phase conductor configuration is used to enable a direct comparison of the electric field intensity and magnetic flux density estimates generated by the two ANN models to measurement results obtained over the lateral profile. Further investigation of ANN models involves an extensive study whereby 13 different overhead transmission lines of horizontal configurations are used as the basis for comparing measurement results to estimates provided by the ANN models. In this study, the performance analysis of the ANN models was evaluated using coefficient of determination and root mean square error. The obtained results demonstrate that the considered ANN approach can be used to estimate the electric field intensity and magnetic flux density in the proximity of overhead transmission lines.