Abstract
The relentless growth of electrical power systems mandates the development of efficient fault detection mechanisms to ensure the reliability and stability of the grid. This research presents a novel approach to three-phase fault detection using a neural network controller implemented within the MATLAB Simulink environment. The proposed model leverages the capabilities of neural networks to accurately identify and classify faults in real-time, contributing to the robustness of power system operation. The heart of the developed solution is a meticulously designed neural network architecture, trained on a comprehensive dataset generated through meticulous simulations of various fault scenarios. Leveraging the advantages of deep learning, the neural network demonstrates its proficiency in discriminating between healthy and faulted system states with high precision. The model's adaptability and capability to handle noise and dynamic variations in fault characteristics underline its efficacy in practical deployment.
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