%0 Dataset
%T Distribution Network Topology Reconfiguration for Adversarial Scenario Generation Simulation Dataset
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/7b8aeda7-17fc-46e4-b2fd-d8838b57d18a
%W NCDC
%R 10.12072/ncdc.db7489.2026
%A yu wen wu
%K Electrical engineering; smart grid; power-system resilience; artificial intelligence; machine learning;
%X This dataset addresses critical-load survival in distribution systems under sequential extreme events. Based on a 7-bus test system and the IEEE 123-bus distribution system, it provides multidimensional time-series scenarios including load demand, distributed generation outputs from photovoltaic and wind power, and line damage states. Combined with remote control switch actions, power-flow calculation, microgrid operational constraints, and reward evaluation, the dataset supports training and evaluation of topology reconfiguration policies using adversarial scenario generation network enhanced distributionally robust deep reinforcement learning. It can be used for distribution-system resilience defense, critical-load survival, extreme-event scenario generation, reinforcement-learning training, and comparative algorithm studies.