Abstract
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 multi-circuit 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.
Highlights
T HE effects of low-frequency magnetic fields on the health of people that reside and/or work near overhead transmission lines have been of research interest for several decades
The proposed method can be used to analyze the multi-circuit overhead transmission lines where the applied current intensities per circuit are not necessarily equal. This method relies on the Artificial Neural Network (ANN) model that is trained to estimate the magnetic flux density vector components for a range of single-circuit overhead transmission lines
If the values of the phase current intensities associated with the individual circuits differ from the current intensity used during the ANN training, appropriate scaling of the ANN outputs is performed
Summary
T HE effects of low-frequency magnetic fields on the health of people that reside and/or work near overhead transmission lines have been of research interest for several decades. A novel method is proposed for estimation of magnetic flux density in the vicinity of multi-circuit overhead transmission lines. The rest of the paper is organized as follows: In Section II the proposed model for estimation of magnetic flux density in the vicinity of multi-circuit overhead transmission line is described in detail. A fully connected feed-forward artificial neural network model is developed to estimate magnetic flux density for a range of different transmission line conductor configurations. The proposed model is designed to provide the magnetic flux density estimate for a given overhead transmission line conductor geometry at a point 1 m above the ground surface and some lateral distance away from the central vertical line. SCG learning algorithm is designed with the primary objective of avoiding the time-consuming line search that is commonly employed by other conjugate gradient algorithms to evaluate the optimal step size and as such, it is shown to be efficient in instances when the neural network model consists of a large number of free weights [40]
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