A Data-Driven Disaster Management Framework for Power Resilience Enhancement

The increasing frequency and severity of extreme weather events lead to growing vulnerability of power systems, which may result in huge economic losses and threats to public safety. Traditional disaster management methods based on very few empirical data have limitations in addressing complex disastrous scenarios. Fortunately, recent advancements in smart sensors and metering infrastructure have enabled the collection of vast amounts of data concerning power system emergencies, providing new opportunities for data-driven approaches. This paper presents a data-driven disaster management framework for enhancing power system resilience. We systematically analyze the application of data-driven technologies in disaster management, including four interdependent stages: disaster prediction and proactive reinforcement, risk assessment, emergency response, as well as post-event service restoration. The application scenarios and inherent advantages of data-driven technologies in stage-wise on-emergency operations are investigated, demonstrating their critical roles in fortifying the resilience of power systems. Case studies on IEEE 24-bus test system have confirmed the effectiveness of proposed data-driven framework for handling strong wind disasters, showing its great potential to improve the preparedness and resistance of power systems against extreme occasions.