POWER TRANSMISSION LINE MODELING USING ARTIFICIAL NEURAL NETWORKS IN THE PRESENCE OF NON-FUNDAMENTAL FREQUENCIES

Abstract

This paper presents a new method for neural modeling of power transmission lines particularly for non-fundamental frequencies. The developed Artificial Neural Network (ANN) is based on the comparison of finitely segmented models to uniformly distributed model to identify the appropriate segmented model representing the power line at required frequencies for different levels of accuracy. The line model performance is characterized through waveform including attenuation and phase shift. The simulated results with ANN-based environment parameter variations are compared with numerical methods and validated experimental. The study demonstrates the efficiency and robustness of our proposed method.