AI-DRIVEN ADAPTIVE PROTECTION SCHEMES FOR RESILIENT POWER SYSTEMS WITH HIGH PENETRATION OF DISTRIBUTED ENERGY RESOURCES

Abstract

The increasing integration of distributed energy resources (DERs), including solar photovoltaic systems, wind energy installations, battery energy storage systems, and electric vehicles, has significantly transformed modern power systems. While these resources improve sustainability and grid flexibility, they introduce substantial challenges to conventional protection schemes due to bidirectional power flows, variable fault currents, and dynamic operating conditions. This study proposes an artificial intelligence-driven adaptive protection framework designed to enhance the resilience, reliability, and operational security of power systems with high DER penetration. Four protection scenarios were evaluated, including a conventional protection system and three progressively advanced AI-assisted adaptive protection configurations. Anticipated outcomes were generated using established power system protection principles, machine learning concepts, and smart grid operational characteristics. The predictive framework suggests that AI-enabled adaptive protection systems may significantly improve fault detection accuracy, fault isolation speed, system reliability, restoration efficiency, and grid resilience while reducing false tripping events and outage durations. The proposed framework serves as a conceptual model and methodological template for future experimental and simulation-based investigations in intelligent power system protection.