AI-Based Fault Detection in Power Transmission Networks

Abstract

Reliable operation of power transmission networks is essential for ensuring uninterrupted electricity supply in modern power systems. However, these networks are highly susceptible to faults caused by line disturbances, insulation failures, overload conditions, and environmental factors, which can lead to significant power outages and system instability. Traditional fault detection methods often rely on threshold-based protection systems that may not provide fast and accurate diagnosis under complex grid conditions. This study presents an AI-based fault detection framework for power transmission networks aimed at improving fault identification accuracy, response time, and system reliability. The methodology involves the acquisition of real-time electrical parameters such as voltage, current, frequency, and impedance from transmission line sensors and smart grid monitoring devices. The collected data is preprocessed and used to train machine learning and deep learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks for temporal fault pattern recognition. Feature engineering techniques are applied to extract relevant fault signatures, enabling classification of different fault types such as single-line-to-ground, line-to-line, and three-phase faults. The performance of the proposed system is evaluated using metrics such as accuracy, precision, recall, F1-score, and detection latency. The results demonstrate that AI-based models significantly outperform conventional protection schemes in terms of fault detection speed and classification accuracy. Among the tested models, LSTM shows superior performance in capturing temporal dependencies in fault signals. The study concludes that AI-driven fault detection systems provide a robust and intelligent solution for enhancing the stability and resilience of power transmission networks. These findings support the advancement of smart grid technologies for more reliable and efficient power system operation.