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By: Kazi Kutubuddin Sayyad Liyakat.
Professor & Head , Department of Electronics and Telecommunication Engineering, BMIT, Solapur, India
Modern electricity infrastructure is undergoing a transformation thanks to the combination of artificial intelligence (AI) and the Internet of Things (IoT), especially in the development of self- healing grid systems. In the context of smart grid automation and resilience, this article provides a thorough review of AI-driven IoT infrastructures. By leveraging distributed sensor networks, real-time data acquisition, and advanced KSK Approach, and machine learning algorithms—includes ANN, DT and K-NN—the proposed framework enables proactive fault detection, isolation, and service restoration (FDIR) with minimal human intervention. IoT devices facilitate seamless communication across distributed energy resources (DERs), smart meters, and phasor measurement units (PMUs), generating high-resolution operational telemetry. AI models process this big data to predict potential failures, optimize reconfiguration pathways, and dynamically balance load distribution under anomalous conditions. The system demonstrates enhanced situational awareness, reduced mean time to repair (MTTR), and improved grid stability through autonomous decision-making loops. Simulation results using IEEE 33-bus and 119-bus test systems validate the efficacy of the AI-IoT synergy in improving fault localization accuracy (>98%) and reducing outage durations by up to 75% compared to conventional SCADA-based approaches. This work underscores the transformative potential of intelligent, data-centric paradigms in evolving the electric grid into a self-sustaining, adaptive cyber- physical system.
Keywords – AI-driven IoT, Self-Healing Grid, Smart Grid Resilience, Edge Intelligence, Reinforcement Learning
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