Exploration of Edge Computing-Based Data-Driven Models in Self-Healing Grids

A bstract-S elf- Healing Grid, an intelligent grid system with self-monitoring and self-repair capabilities, is critical for enhancing the reliability and robustness of power systems. However, self-healing grid technology faces numerous challenges, such as insufficient dynamic response, limitations of centralized architectures, and complexity in fault recovery paths. Edge computing, a distributed computing paradigm that brings data processing capabilities closer to the source, effectively reduces data transmission latency and improves real-time system responsiveness. By integrating edge computing with data-driven models, rapid fault diagnosis and dynamic response optimization can be achieved, advancing the intelligence level of self-healing grids. This paper explores the application prospects of edge computing technology in self-healing grids, analyzes its technical background and key challenges through case studies, and proposes future research directions and solutions, aiming to provide theoretical support for the research and development of self-healing grid technology.