1.Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province (Yanshan University), Qinhuangdao 066004, China;2.School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;3.Xianyou Branch of Putian Power Supply Service Company, Putian 351200, China

China is one of the countries that suffered the most from typhoon disasters. Strong typhoons can lead to mass disconnection and tower collapse, which can then evolve into electric disasters, and the risks cannot be ignored. In response to typhoon disasters, the PageRank algorithm is used to establish a distribution network failure-rate model that takes into account the relevance between disconnection and tower collapse. Meanwhile, for the more complex part of meteorological and geographic information in the model, the data-driven method is used to introduce the information disturbance factor μ into the relevant distribution network failure-rate model. The mapping relationship between meteorological and geographic information and μ is built through the radial basis function (RBF) neural network model, and the driving results are verified and corrected. Finally, a new quantitative index of resilience is proposed, which takes the optimum index and maximized economy as the upper and lower objective functions, and a two-level planning model for the siting and sizing of flexible resources in typhoon disasters is constructed considering the relevance impact. By coordinating the planning of flexible resources, the distribution network achieves the economic optimum while increasing its resilience. The accuracy and effectiveness of the model are verified by calculation examples.