本数据集面向顺序极端事件下配电系统关键负荷生存问题，基于7节点测试系统和IEEE 123节点配电系统，构建负荷需求、分布式电源输出（光伏、风电）和线路损伤状态等多维时间序列场景，并结合远程控制开关动作、潮流计算、微电网运行约束和奖励评价，用于训练与评估基于对抗场景生成网络（AS-GAN）增强的分布鲁棒深度强化学习（DR-DRL）拓扑重构策略。该数据集可用于配电网韧性防御、关键负荷保持、极端事件场景生成、强化学习算法训练和模型对比实验。
\n数据实体文件构成：本数据集包含2个Excel表格文件，分别为Case7_Scenario.xlsx和IEEE123_Scenario.xlsx。其中，Case7_Scenario.xlsx和IEEE123_Scenario.xlsx为配电网拓扑重构仿真场景数据文件，分别对应7节点测试系统和IEEE 123节点配电系统的多维时间序列场景数据。
\nCase7_Scenario.xlsx为7节点配电系统仿真场景数据文件，包含表头要素说明、load、pv、wp和disruption等5个工作表。其中，load表记录24个时段的负荷用电量数据，字段命名为load_t01至load_t24，单位为kWh；pv表记录24个时段的光伏发电量数据，字段命名为pv_t01至pv_t24，单位为kWh；wp表记录风电出力相关数据，字段命名为wp_t01至wp_t25，其中主要字段表示风电发电量或归一化风电出力；disruption表记录扰动/中断状态数据，字段命名为disruption_t01_e01至disruption_t24_e05，共覆盖24个时段、每个时段5个扰动元素，数值为0或1，0表示正常或未中断，1表示发生扰动或中断。各数据表第1行为字段名称，第2行为单位或物理含义说明，第3行开始为原始数值数据，每一行表示一个仿真场景或样本。
\nIEEE123_Scenario.xlsx为IEEE 123节点配电系统仿真场景数据文件，包含表头要素说明、load、pv、wp和disruption等5个工作表。其中，load表记录24个时段的负荷用电量数据，字段命名为load_t01至load_t24，单位为kWh；pv表记录6组光伏资源单元在24个时段内的发电量数据，字段命名为pv_01_t01至pv_06_t24，单位为kWh；wp表记录6组风电资源单元在24个时段内的发电量数据，字段命名为wp_01_t01至wp_06_t24，单位为kWh；disruption表记录扰动/中断状态数据，字段命名为disruption_t01_e01至disruption_t24_e05，共覆盖24个时段、每个时段5个扰动元素，数值为0或1。上述场景数据可用于配电网拓扑重构、关键负荷生存分析、极端事件扰动场景生成、强化学习训练及模型性能对比评估。
", "ds_source": "本数据集为完全仿真生成数据，不源自特定文献、实测或第三方下载。系统文件包括Case7_Data.xlsx和IEEE123_Data.xlsx；场景文件包括Case7_Scenario.xlsx和IEEE123_Scenario.xlsx，包含load、pv、wp和disruption等时间序列表。代码按场景ID和时间步读取负荷、光伏、风电和线路损伤状态，并在Pandapower电力网络环境中仿真。
", "ds_process_way": "基于Python、PyTorch、Gymnasium和Pandapower构建强化学习仿真环境，读取系统与场景Excel文件后，将每个场景组织为包含负荷、光伏、风电和脆弱线路状态的多维时间序列；通过远程控制开关动作更新配电网拓扑，执行潮流计算，并根据关键负荷是否与分布式电源连通、非法开关动作、电压越限、线路损耗和功率不平衡等指标计算奖励；采用DQN或PPO训练拓扑重构策略，并通过TTN增强的AS-GAN生成对抗场景，迭代加入训练集以提升策略鲁棒性；Julia/JuMP/Gurobi代码用于确定性优化和两阶段随机优化对比。
", "ds_quality": "数据表结构清晰，系统数据与场景数据分离；场景按统一时间步组织，每个场景覆盖24小时；训练、样本内测试和样本外测试可由代码中的场景索引划分；仿真过程中通过潮流收敛、开关动作合法性、关键负荷连通性、奖励值、存活率、均值和标准差等指标进行评估。需要注意的是，该数据集为仿真生成数据，不是实际灾害、实际停电或真实地理观测数据，不能直接代表具体地区的真实电网运行状态。
", "ds_acq_start_time": "2023-01-01 00:00:00", "ds_acq_end_time": null, "ds_acq_place": "南京", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 843590, "ds_files_count": 3, "ds_format": ".xlsx", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "7b8aeda7-17fc-46e4-b2fd-d8838b57d18a.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "虞文武", "ds_serv_phone": "15051861330", "ds_serv_mail": "wwyu@seu.edu.cn", "doi_value": "", "subject_codes": [ "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:58:39", "last_updated": "2026-07-21 09:19:51", "protected": false, "protected_to": "2028-06-30 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.db7489.2026", "i18n": { "en": { "title": "Distribution Network Topology Reconfiguration for Adversarial Scenario Generation Simulation Dataset", "ds_format": ".xlsx", "ds_source": "This dataset is completely synthetically generated through simulation and does not originate from specific literature, field measurements, or third-party downloads.\r\nSystem files include Case7_Data.xlsx and IEEE123_Data.xlsx with bus_data, line_data, load_data, sgen_data, slack_data, switch_data, and geo_data sheets. Scenario files include Case7_Scenario.xlsx and IEEE123_Scenario.xlsx with load, pv, wp, and disruption time-series sheets. The code loads load profiles, photovoltaic outputs, wind-power outputs, and line-damage states by scenario ID and time step, and simulates them in a Pandapower-based power-network environment.", "ds_quality": "The dataset has a clear tabular structure separating system data and scenario data. Scenarios are organized with a unified time-step structure, and each scenario covers a 24-hour horizon. Training, in-sample testing, and out-of-sample testing can be split through scenario indices in the code. Simulation quality is evaluated through power-flow convergence, switch-action feasibility, critical-load connectivity, reward values, survival rate, mean values, and standard deviations. The dataset is simulation-generated and does not represent real disaster records, real outage measurements, or real geographic observations; it should not be directly interpreted as the actual operating state of a specific regional grid.", "ds_ref_way": "", "ds_abstract": "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.", "ds_time_res": "", "ds_acq_place": "Nanjing", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The processing workflow builds a reinforcement-learning simulation environment using Python, PyTorch, Gymnasium, and Pandapower. After reading the system and scenario Excel files, each scenario is organized as a multidimensional time series containing load, photovoltaic output, wind-power output, and vulnerable-line status. Remote control switch actions update the distribution-system topology, power flow is calculated, and rewards are evaluated using critical-load connectivity to distributed generation, illegal switch operations, voltage violations, line losses, and power imbalance. DQN or PPO is used to train topology reconfiguration policies, while a TTN-enhanced AS-GAN generates adversarial scenarios that are iteratively added to the training set to improve robustness. Julia/JuMP/Gurobi code is used for deterministic and two-stage stochastic optimization comparisons.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "allow_update_data": false, "ds_topic_tags": [ "配电网韧性；拓扑重构；关键负荷生存；顺序极端事件；对抗场景生成；分布鲁棒深度强化学习；网络化微电网" ], "ds_subject_tags": [ "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "虞文武", "email": "wwyu@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" } ], "category": "其他" }