Dataset for Transient Stability Assessment of IEEE 39-Bus System

Published: 20 December 2024| Version 1 | DOI: 10.17632/p992nhb8ss.1
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Description

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.

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Institutions

Elektrotehnicki Institut Nikola Tesla

Categories

Power Engineering, Machine Learning, Feature Extraction, Time Series, Transient Stability of Power System, Deep Learning, Binary Classification

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