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This dataset contains phasor measurements (PMU-type) signals from the IEEE New England 39-bus power system test case network, which are generated from a large corpus of systematic MATLAB®/Simulink electro-mechanical transients simulations. It was prepared to serve as a convenient and open database for experimenting with different types of machine learning (including deep learning) techniques for transient stability assessment (TSA) of electrical power systems. A dataset contains time-domain signals from 9360 simulations. Different load and generation levels of the New England 39-bus benchmark power system are systematically covered, as well as all three major types of short-circuit events (three-phase, two-phase and single-phase faults) in all parts of the network. The consumed power of the network was set to 80%, 90%, 100%, 110% and 120% of the basic system load levels (for different system load levels, both generation and loads are scaled by the same ratio). The short-circuits are located on the busbar or on the transmission line (TL). When they are located on a TL, it was assumed that they can occur at 20%, 40%, 60%, and 80% of the line length. Timing of the fault occurrences takes into the consideration a moment on the instantaneous sinusoidal reference voltage. The observation period of each simulation was set at 3 seconds and signals are sampled at 1/60 s resolution. Many different machine electrical and mechanical (rotor and stator quantities), as well as network (three-phase currents and voltages), time-domain signals are obtained from simulations with a PMU-type resolution. A dataset is a collection of MAT files (.mat) which can be imported into the MATLAB® Workspace. The database consists of 9360 simulations (15x624 simulations) separated into ".mat" files. Every database or packet of 624 simulations has a different nickname. Nickname is an abbreviation for a specific condition that has been observed (i.e. simulated) in that specific case. Filenames reveal network conditions. Load denotes load level of the system in % and SC denotes type of short-circuit where 1, 2 and 3 are single-phase, double-phase and three-phase short-circuit, respectively. For example, Load_80_SC_1_OUTPUT.mat filename has the following meaning: Load_80 means that the consumed power was set to 80% of the basic system load level and SC_1 means that a single-phase short circuit has been observed. List of variable names: Angle_Vabc -- Bus Phase Angles for phase A, B and C (repetitively for different buses) EFD -- EFD in PU LA -- Power Load Angle in degrees Magnitude_Vabc -- Bus Voltage Magnitudes for phase A, B and C (repetitively for different buses) P -- Electrical Power in PU Pe -- Generator Active Power in PU Qe -- Generator Reactive Power in PU SI_id -- Stator d-component Current in PU Si_iq -- Stator q-component Current in PU STOP -- Transient Stability Index (TSI), 0 - in synchronism, 1 - out of synchronism SV_vd -- Stator d-component Voltage in PU SV_vq -- Stator q-component Voltage in PU Vt -- Stator Voltage in PU d_theta -- Rotor Angle Deviation in radians t -- Simulation time, i.e. sampling time theta -- Rotor Mechanical Angle in degrees w -- Rotor Speed in PU License: Creative Commons CC-BY Disclaimer: This dataset is provided "as is", without any warranties of any kind.
{"references": ["Sarajcev, P.; Kunac, A.; Petrovic, G.; Despalatovic, M. Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble. Energies 2021, 14, 3148. https://doi.org/10.3390/en14113148"]}
Funding acknowledgement: Project IP-2019-04-7292 - Power system disturbance simulator and non-sinusoidal voltages and currents calibrator, funded by the Croatian Science Foundation, Republic of Croatia.
transient stability, power system, machine learning, PMU signals, deep learning, New England test case, MATLAB/Simulink
transient stability, power system, machine learning, PMU signals, deep learning, New England test case, MATLAB/Simulink
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