Dynamic Power Flow Pattern Analysis for Fault Risk Prediction in Renewable-Integrated Distribution Networks

Abstract

The large-scale integration of renewable energy resources has fundamentally reshaped the operational characteristics of modern power distribution networks. While distributed generation improves system sustainability, it also introduces significant uncertainty and complexity into power flow regulation and fault management. Conventional monitoring approaches often lack sufficient sensitivity to capture emerging risks in highly decentralized grid environments. To address this challenge, this paper proposes a novel fault early-warning framework for renewable-rich distribution systems based on complex network modeling. By analyzing network topology and power flow interactions, a mesoscopic representation termed dynamic power flow patterns is developed to characterize evolving operational states under varying generation and load conditions. Building upon this representation, the proposed method evaluates network vulnerability and fault propagation tendencies by integrating electrical betweenness indicators and dynamic structural metrics. Extreme disturbance scenarios, including large-scale line outages caused by natural disasters, are simulated to investigate system resilience and cascading failure behavior. Through comprehensive case studies, the dynamic evolution of network states during widespread disruptions is examined. The results confirm that the proposed framework can effectively identify critical risk patterns and provide timely warnings prior to severe performance degradation. This study offers a new analytical perspective for enhancing reliability and operational security in next-generation sustainable power distribution systems.