An Intelligent Deep Learning Framework for Real-Time Fault Detection in Smart Grids
TL;DR
Results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
Abstract
ABSTRACT -The increasing integration of renewable energy resources, distributed generation, electric vehicles, and intelligent monitoring devices has significantly enhanced the complexity of modern smart grids, making conventional fault detection techniques inadequate for ensuring reliable and secure power system operation. This paper presents an AI-based fault detection framework for smart grids that utilizes machine learning and deep learning techniques to identify, classify, and localize electrical faults in real time. The proposed framework collects operational data from intelligent electronic devices (IEDs), phasor measurement units (PMUs), smart meters, and IoT-enabled sensors. The acquired data undergo preprocessing, normalization, and feature extraction before being analyzed using a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The CNN effectively extracts spatial fault characteristics, while the LSTM captures temporal variations in electrical signals for accurate fault prediction. The trained model is deployed on an edge-cloud architecture to enable low-latency fault detection, rapid decision-making, and remote monitoring. Experimental evaluation demonstrates that the proposed system achieves high fault detection accuracy, reduced false alarm rates, and faster response times compared with conventional rule-based and statistical approaches. The framework also enhances grid reliability, minimizes outage duration, supports predictive maintenance, and improves operational efficiency under dynamic grid conditions. These results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures. Keywords— Smart Grid, Artificial Intelligence (AI), Fault Detection, Deep Learning, Machine Learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Internet of Things (IoT), Predictive Maintenance, Edge Computing.