Methodological Framework and System Modeling for ANN-Based Transmission Line Fault Detection

Following the comprehensive review of machine learning applications in power systems, the next critical phase in developing an intelligent fault diagnosis system is the establishment of a robust simulation framework. This paper presents the methodological progress and system modeling required to generate a training dataset for an Artificial Neural Network (ANN). A detailed 11kV three-phase transmission line model is designed using the MATLAB/Simulink environment. The primary objective of this phase is to establish a controlled digital environment where various asymmetrical and symmetrical shunt faults can be simulated under varying conditions. Furthermore, this paper outlines the data acquisition strategy, focusing on the extraction of Root Mean Square (RMS) values of three-phase voltages and currents, which will serve as the foundational feature vectors for the proposed ANN architecture. This framework bridges the gap between theoretical literature and final empirical analysis, setting the stage for intelligent fault classification.