GRID MODERNIZATION: FROM TRADITIONAL TO AI-OPTIMIZED SELF-HEALING NETWORKS

Abstract

The modern electrical power grid faces unprecedented challenges from renewable energy integration, electrification of transport and buildings, extreme weather events, and cyber-physical threats, necessitating a shift from traditional centralized, unidirectional systems to intelligent, resilient architectures. Grid modernization progresses through distinct phases: the smart grid paradigm introduces advanced metering infrastructure (AMI), phasor measurement units (PMUs), and standardized communication protocols for bidirectional flows and enhanced observability; self-healing mechanisms enable autonomous fault location, isolation, and service restoration (FLISR) via multi-agent systems and automated switching; and AI-optimized networks represent the pinnacle, leveraging supervised machine learning for fault classification, unsupervised anomaly detection, reinforcement learning (including deep RL and multi-agent variants) for adaptive reconfiguration policies, graph neural networks for topology-aware decisions, and hybrid physics-informed approaches for robust performance under uncertainty. These AI techniques facilitate proactive predictive maintenance, real-time detection and localization, autonomous isolation/reconfiguration, and rapid restoration, significantly reducing outage durations, energy not supplied, and traditional reliability indices like SAIDI/SAIFI while bolstering resilience. This paper reviews the evolutionary pathway, fundamentals of self-healing, detailed AI taxonomy and applications across the self-healing cycle, comparative performance analysis, recent 2023-2026 advances (such as millisecond-scale DRL rerouting, edge-AI, and digital twin integration), and persistent challenges including data quality, cybersecurity vulnerabilities, interoperability with standards (IEC 61850, IEEE 1547), explainability, and equity. By addressing these gaps through future directions like large language model integration, quantum-inspired optimization, and AI-blockchain for DER coordination, AI-optimized self-healing networks promise near-zero downtime, alignment with net-zero goals, and a sustainable, equitable energy future.

Keywords : Smart Grid, Self-Healing Networks, Artificial Intelligence, Reinforcement Learning, Grid Resilience.