Dataset for Transient Stability Assessment of IEEE 39-Bus System

This dataset contains 50 features and was generated through 12,852 time-domain simulations performed on the IEEE New England 39 bus system test case using DIgSILENT PowerFactory and Python automation. The simulations span diverse operating conditions by varying the generation/load profile from 80% to 120% in 5% increments. For each condition, three-phase short-circuit faults were applied at seven distinct locations (0%, 10%, 20%, 50%, 80%, 90%, 100%) along all transmission lines, with fault clearing times ranging from 0.1s to 0.3s.

Key features captured for each of the 10 generators (G02 is the reference machine) include:

P in MW - Active Power
ut in p.u. - Terminal Voltage
ie in p.u. - Excitation Current
xspeed in p.u. - Rotor Speed
firel in deg - Rotor Angle (relative to G02)

Simulations lasted 10 seconds to ensure accurate transient stability assessment. Post-fault data was sampled every 0.01s from fault clearance up to 0.6s afterward, labeling the stability state as 1 (stable) or 0 (unstable). The dataset generation process took 5,840 seconds. The dataset exhibits a class imbalance, with 42% of cases belonging to the unstable class. All simulation data were exported to .csv files and subsequently unified into a single pickle file (tsa_data.pkl).

Helper scripts are provided:

dataset_loader.py: Includes the load_tsa_data function to load the dataset.
usage.py: Demonstrates how to use the loader module.

This dataset serves as a comprehensive foundation for machine learning applications in transient stability assessment (TSA), offering insights into system behavior under dynamic conditions.

本数据集包含50个特征，其生成基于对IEEE新英格兰39节点系统测试案例（IEEE New England 39 bus system test case）开展的12852次时域仿真，仿真通过DIgSILENT PowerFactory与Python自动化脚本完成。

仿真通过以5%为步长将发电/负荷曲线从80%调整至120%，覆盖多样的运行工况。针对每种工况，在所有输电线路的7个不同位置（线路长度的0%、10%、20%、50%、80%、90%、100%处）施加三相短路故障，故障切除时间范围为0.1s至0.3s。

针对10台发电机（以G02为参考机）采集的核心特征包括：
- P（单位：MW）：有功功率
- ut（单位：标幺值（p.u.））：机端电压
- ie（单位：标幺值（p.u.））：励磁电流
- xspeed（单位：标幺值（p.u.））：转子转速
- firel（单位：度（deg））：转子角（相对于G02）

本次仿真总时长为10秒，以确保暂态稳定评估（Transient Stability Assessment, TSA）的准确性。故障后数据从故障切除时刻起，以0.01s的采样间隔采集至故障发生后0.6s，将系统稳定状态标记为1（稳定）或0（不稳定）。本数据集的生成耗时5840秒。数据集存在类别不平衡问题，其中42%的样本属于不稳定类别。所有仿真数据先导出为.csv文件，后统一整合为单个Pickle格式文件（tsa_data.pkl）。

配套提供以下辅助脚本：
1. dataset_loader.py：包含load_tsa_data函数，用于加载本数据集。
2. usage.py：演示如何使用该加载器模块。

本数据集可作为暂态稳定评估（TSA）领域机器学习应用的综合性基础数据集，可为动态工况下的系统行为分析提供参考依据。

搜集汇总

背景与挑战

背景概述

该数据集包含通过12,852次时域模拟生成的50个特征，覆盖了IEEE 39总线系统在不同发电/负载工况下的暂态稳定性评估。数据集主要用于机器学习和深度学习应用，提供了系统在动态条件下的行为洞察。