An Intelligent Artificial Neural Network Framework for Low-Latency Fault Detection and Precise Localization in Electrical Transmission Lines

This paper has defined the concept and planned methodology for an intelligent ANN framework that simultaneously detects transmission-line faults, classifies their type, and estimates their location using a short single-ended measurement window. The proposed 220 kV, 150 km MATLAB/Simulink environment will generate diverse healthy, transient, and fault records under controlled variations in location, resistance, inception angle, source condition, load, frequency, and noise. A compact multi-task ANN will fuse learned temporal projections with physics-guided features and will provide three coordinated outputs through a shared encoder. The planned evaluation gives equal importance to protection performance, generalization, robustness, and total decision latency. Grouped data splitting, held-out-condition testing, model compression, and deterministic supervisory logic are included from the beginning of the design. The work is therefore positioned as a practical research framework rather than a performance claim. Its final value will depend on transparent validation, comparison with established baselines, and progressive movement from simulation to real or real-time test data.

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