Resilience Assessment of Urban Distribution Network Under Heavy Rain: A Knowledge- Informed Data-Driven Approach
Ke Li, Jie Ma, Jianlong Gao, Changqing Xu, Wenfeng Li, Yubin Mao, Shigong Jiang
Abstract
Heavy rains pose a great threat to the reliable and secure power supply of urban distribution networks. A knowledge-informed data-driven resilience assessment approach is proposed to evaluate urban distribution networks’ abilities to resist heavy rains. Firstly, the rainstorm waterlogging process is simulated to obtain the rainstorm intensity and rainfall process, providing the input for the data-driven model. Then, input variables are grouped guided by expert knowledge, and a dynamic and static data-driven model is constructed to predict the line outages based on historical data. Finally, the Monte Carlo simulation method integrated with the data-driven model is developed to assess the resilience of urban distribution networks and the number of line outages is selected as the evaluation metric. The effectiveness of the proposed method is sufficiently validated by the historical data of an urban distribution network.