Risk analysis of distribution network outages under a typhoon–rainstorm–flood disaster chain

Abstract

The typhoon–rainstorm–flood disaster chain poses a significant flooding risk to urban distribution network (DN) equipment, often leading to power system outages. The increasing frequency and severity of this disaster chain in East Asia, driven by global warming, population growth, and land-use changes, highlight the need for improved disaster preparedness. Traditional studies focusing on individual meteorological disasters, such as typhoons or floods, may be insufficient for developing efective mitigation strategies. To address this gap, this study proposes a novel risk analysis method for enhancing the disaster defence strategy of DNs. First, a hybrid deep learning model is developed to forecast a 48-h rainstorm time series following a typhoon's landfall. Second, a one-dimensional pipe network and a two-dimensional surface-coupled urban flood model are constructed to predict flood depth based on the typhoon–rainstorm time series. Third, an influence factor set is established from environmental and societal perspectives, and spatial correlation analysis is applied to assess DN outage risk. To validate the proposed method, Typhoon Talim (2023), which made landfall in China, is used as a case study. The results demonstrate that the model effectively captures disaster-causing mechanisms and accurately identifies high-risk areas. This research provides a theoretical foundation for outage risk prevention in developing countries, particularly in mitigating the impacts of the typhoon–rainstorm–flood disaster chain.