AI-Powered Edge Impedance Analysis for Risk Awareness in Smart Distribution Networks: Methodology and Validation

With the rapid development of smart grids, distribution networks are becoming increasingly intelligent and complex. However, their structural diversity and high operational uncertainty present significant challenges to ensuring secure and stable performance. To address these issues, this paper presents an AI-powered edge impedance analysis approach for risk situation awareness in smart distribution networks. The proposed method leverages edge computing to conduct real-time impedance-based monitoring and integrates artificial intelligence to enhance the real-time identification of abnormal conditions using AI-assisted analysis, classification, and fault prediction capabilities. First, the operational characteristics and risk types of distribution networks are systematically analyzed. Then, an AI-driven framework combining edge impedance sensing with intelligent analysis models is designed. Finally, a case study is conducted to validate the effectiveness and practicality of the proposed approach. The results demonstrate that this method significantly improves the timeliness and accuracy of risk awareness, offering valuable support for smart grid operation and management.