AI-Driven Adaptive Smart Grid for Real-Time Energy Optimization and Fault Detection

Abstract: Conventional smart-grid systems often rely on fixed control strategies that have limited ability to respond dynamically to changing electricity demand, renewable-energy variability, equipment degradation, and unexpected faults. This study proposes an AI-Driven Adaptive Smart Grid (AI-ASG) for real-time energy optimization and automated fault detection. The framework integrates machine learning, real-time sensing, adaptive demand-response control, and intelligent fault classification to improve grid efficiency, reliability, and operational responsiveness. A mixed-methods quantitative and qualitative research design was adopted. Smart-grid operational data, including load demand, voltage, current, frequency, power factor, renewable-energy generation, and equipment-fault indicators, were processed through preprocessing, feature extraction, and AI-based prediction. Adaptive optimization was evaluated using energy consumption reduction, peak-load reduction, renewable-energy utilization, power-loss reduction, voltage stability, fault-detection accuracy, precision, recall, F1-score, false-alarm rate, detection latency, computational overhead, and system scalability. Qualitative assessment considered interpretability, operator usability, interoperability, adaptability, and decision-support effectiveness. Performance was compared with conventional rule-based control, non-adaptive machine-learning control, and the proposed adaptive AI approach. The proposed AI-ASG demonstrated improved operational performance across energy-management and fault-detection measures. Quantitative evaluation indicated reductions in unnecessary energy consumption and peak demand, improved renewable-energy utilization and voltage stability, and faster fault identification. The adaptive model also achieved higher fault-classification accuracy, precision, recall, and F1-score while reducing false alarms and detection latency compared with conventional approaches. Qualitative findings indicated improved system interpretability, operator confidence, adaptability, and real-time decision support. The proposed AI-driven adaptive architecture provides an integrated approach for intelligent energy optimization and real-time fault management, supporting more responsive, reliable, efficient, and scalable smart-grid operation. Keywords: artificial intelligence; fault detection; Adaptive Smart Grid; Energy Optimization; Real-Time Power Systems.