An Improved AI-Driven LVAC Network Fault Detection and Classification

The increasing penetration of low-carbon technologies (LCTs) has significantly altered fault characteristics in low-voltage alternating current (LVAC) distribution networks, challenging conventional protection schemes and motivating the need for robust fault detection and classification (FDC) with limited monitoring facilities. This paper proposes an AI driven and hybrid convolutional neural networks (CNN)- long short-term memory (LSTM)-Transformer framework for LVAC FDC. The architecture integrates convolutional layers for channel-wise feature enhancement, LSTM networks for modelling short-term fault transients, and