Research on fault scenario prediction and resilience enhancement strategy of active distribution network under ice disaster

Abstract Active distribution network (ADN) will encounter severe large-scale blackouts under extreme ice disaster. Different from the passive resilience restoration method after the real fault occurs, an information entropy model-based resilience enhancement strategy is proposed in this paper, which is dedicated to enhancing resilience before failure occurs. The vulnerability rate model is used to improve the situational awareness of the power system under severe weather. Typical scenario selection model (TSSM) is proposed by information entropy. Proactive defense plan with network reconfiguration at its core is the key to resilience enhancement. Implementation of repair by matching resources and faults, which is a means of resilience enhancement after the disaster has ended. The proposed resilience evaluation model considers the impact of economic factors on resilience enhancement. Different strategies and different evaluation metrics are adopted to evaluate the performance of resilience enhancement. Finally, the practical and simulated disaster testings are acted on the simplified real grid system to verify the method proposed in this paper.