Magnetic Flux Density Harmonics Estimation in the Vicinity of the Overhead Lines Utilizing Artificial Neural Networks

This paper presents a method based on Artificial Neural Networks (ANN) for magnetic flux density harmonics estimation in the vicinity of overhead lines. The proposed method can be employed for the magnetic flux density estimation in the cases of excitation currents with a pure sinusoidal waveform, as well as for excitation currents with harmonically distorted waveforms. The method utilizes two ANNs that are trained in a such way that enables their application for overhead lines of arbitrary phase conductor configurations and arbitrary current harmonic spectrum. In this paper, the proposed method is applied for magnetic flux density harmonics estimation in the vicinity of a typical distribution overhead line. The proposed method is validated by comparison with Biot-Savart based method. The obtained results demonstrate not only the accuracy and effectiveness of the proposed method but also the importance of considering the magnetic flux density harmonics in the vicinity of power facilities.