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Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case

Sarajcev, Petar; Kunac, Antonijo; Petrovic, Goran; Despalatovic, Marin

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  <dc:creator>Sarajcev, Petar</dc:creator>
  <dc:creator>Kunac, Antonijo</dc:creator>
  <dc:creator>Petrovic, Goran</dc:creator>
  <dc:creator>Despalatovic, Marin</dc:creator>
  <dc:description>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.</dc:description>
  <dc:description>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.</dc:description>
  <dc:subject>power system</dc:subject>
  <dc:subject>transient stability</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>deep learning</dc:subject>
  <dc:subject>New England test case</dc:subject>
  <dc:subject>PMU signals</dc:subject>
  <dc:title>Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case</dc:title>
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