Fault Detection and Protection Techniques in Electrical Power Transmission Systems: A Methodology-Based Comparative Study

Abstract

Electrical transmission lines are continuously exposed to short circuits, lightning, insulation deterioration, conductor contact, equipment failure, and environmental disturbances. A protection system must therefore detect a fault rapidly, identify the involved phase or phases, estimate the fault location, and isolate only the affected section. This methodology paper develops a comparative framework for evaluating conventional impedance/distance protection, sequence-component methods, wavelet-based transient analysis, artificial-intelligence classifiers, and traveling-wave protection. The proposed study uses a two-terminal high-voltage transmission-line model and a controlled test matrix covering line-to-ground, line-to-line, double-line-to-ground, and three-phase faults over variations in fault location, resistance, inception angle, and loading. Current and voltage signals are processed using symmetrical components and discrete wavelet features; classification may be implemented using linear discriminant analysis, support vector machines, fuzzy inference, or neural networks. Published benchmark results up to 2016 indicate that intelligent and transient-based methods improve classification robustness, while traveling-wave methods provide the fastest fault discrimination. The framework emphasizes dependability, security, operating time, fault-location error, computational burden, and suitability for practical protective relays.