Neural Network-Based Fault Detection in Power Grids: A Data Driven Approach for Enhanced Grid Reliability

Abstract

The increasing complexity and demand on modern power systems necessitate robust and real-time fault detection mechanisms to ensure grid stability and prevent catastrophic failures. This study investigates the application of artificial neural networks (ANNs) for fault detection in power grids. Using historical grid operation data and simulated fault scenarios, we trained multilayer perceptrons and convolutional neural networks to detect and classify common transmission faults. The models were evaluated on the IEEE 39-bus test system. Results indicate high fault detection accuracy (>97%) with rapid response times (<100 ms), significantly outperforming traditional methods. The proposed framework is scalabl and adaptable, paving the way for smart grid integration and real-time monitoring.