Citation :

Susmit Sen, "

AI-Enabled Substation Architectures for Autonomous Power Systems: Reliability, Asset Intelligence, and Grid-Edge Analytics

Abstract

A swift transformation is being experienced in the electric power sector due to decarbonization, electrification, aging infrastructure, and rising reliability demands. The traditional automation systems used in substations, though useful in monitoring and control, are still highly reactive and silo-based in how they utilize data. New innovations connected to Artificial Intelligence (AI) offer a chance to radically transform substation intelligence and support predictive analytics, autonomous decision-making, and integrated health management of assets. Nevertheless, current deployments are un architecturally coherent, thereby reducing scalability, trust, and operational effects. The proposed paper presents a conceptual and practical architecture of AI-based substations, which combines the concept of Supervisory Control And Data Acquisition (SCADA), grid-edge analytics, machine learning-based intelligence, and governance controls into a single system. The architecture separates substation intelligence into data acquisition, analytics, control, and governance layers, creating effective, auditable, autonomous functionality. The concept of combining the data of protection relays and dissolved gas analysis using AI-based models to reveal the early detection of faults and actionable decision support is illustrated based on an applied reference implementation focused on transformer health intelligence. The paper also analyzes the cybersecurity and governance factors that are critical in the deployment of AI in mission-critical power system settings. The presented framework helps to promote the transition of reactive grid management to resilient, self-healing, and autonomous power systems. The article is concerned with the inadequate architectural solutions to coordinate AI, governance, and grid edge intelligence in contemporary substations.

Keywords

Substation automation, Artificial Intelligence, SCADA, Predictive maintenance, Power system reliability, Autonomous grids.

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