This paper introduces a novel method that leverages artificial neural networks to estimate magnetic flux density in the proximity of overhead transmission lines. The proposed method utilizes an artificial neural network to estimate the parameters of a mathematical model that describes the magnetic flux density distribution along the lateral profile for various configurations of overhead transmission lines. The training target data is acquired using the particle swarm optimization algorithm. A performance comparison between the proposed method and the Biot-Savart law-based method is conducted using an extensive test dataset. The resulting coefficient of determination and mean square error values demonstrate the successful application of the proposed method for a range of different spatial arrangements of phase conductors. Furthermore, the performance of the proposed method is thoroughly assessed on multiple test cases. The practical relevance of the proposed method is highlighted by contrasting its results with the field measurements obtained in the proximity of a 400 kV overhead transmission line.

Abstract 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.

Abstract The methodology for the evaluation of long-term exposure to the overhead line magnetic field is presented, in this paper. The developed methodology is based on the ambient temperature measurements and phase conductors’ height measurements to find a linear regression model to determine phase conductors’ height changes for different ambient temperatures. Based on the overhead transmission line geometry, and datasets about historical overhead line phase current intensity values and ambient temperatures long-term magnetic field exposure can be determined. For magnetic flux density determination, a method based on artificial neural networks is used. The methodology is applied to the case study of overhead line that connect substations Sarajevo 10 and Sarajevo 20. A period of one year is analyzed and magnetic flux density values are determined. The obtained results indicate that during the analyzed period for significant amounts of time magnetic flux density values surpass the recommended values for long-term exposure.