Dataset Open Access
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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<dct:title>Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case</dct:title>
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<dcat:keyword>power system</dcat:keyword>
<dcat:keyword>transient stability</dcat:keyword>
<dcat:keyword>machine learning</dcat:keyword>
<dcat:keyword>deep learning</dcat:keyword>
<dcat:keyword>New England test case</dcat:keyword>
<dcat:keyword>MATLAB/Simulink</dcat:keyword>
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<dct:description><p>This dataset contains phasor measurements (PMU-type) signals from the <strong>IEEE New England 39-bus power system</strong> test case network, which are generated from a large corpus of systematic <strong>MATLAB&reg;/Simulink</strong> electro-mechanical transients simulations. It was prepared to serve as a convenient and open database for experimenting with different types of <strong>machine learning</strong> (including deep learning) techniques for <strong>transient stability assessment</strong> (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.&nbsp;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.</p> <p>A <strong>dataset</strong> is a collection of MAT files (.mat) which can be imported into the MATLAB&reg; Workspace.</p> <ul> <li>The database consists of 9360 simulations (15x624 simulations) separated into &quot;.mat&quot; files.</li> <li>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.</li> <li>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.</li> </ul> <p><strong>List of variable names</strong>:<br> Angle_Vabc -- Bus Phase Angles for phase A, B and C (repetitively for different buses)<br> EFD&nbsp; -- EFD in PU<br> LA -- Power Load Angle in degrees<br> Magnitude_Vabc -- Bus Voltage Magnitudes for phase A, B and C (repetitively for different buses)<br> P -- Electrical Power in PU<br> Pe -- Generator Active Power in PU<br> Qe -- Generator Reactive Power in PU<br> SI_id -- Stator d-component Current in PU<br> Si_iq -- Stator q-component Current in PU<br> STOP -- Transient Stability Index (TSI), 0 - in synchronism, 1 - out of synchronism<br> SV_vd -- Stator d-component Voltage in PU<br> SV_vq -- Stator q-component Voltage in PU<br> Vt -- Stator Voltage in PU<br> d_theta -- Rotor Angle Deviation in radians<br> t -- Simulation time, i.e. sampling time<br> theta -- Rotor Mechanical Angle in degrees<br> w -- Rotor Speed in PU</p> <p><strong>License</strong>:<strong> </strong>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong>: This dataset is provided &quot;as is&quot;, without any warranties of any kind.</p></dct:description>
<dct: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.</dct:description>
<dct:description>{"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"]}</dct:description>
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618
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| All versions | This version | |
|---|---|---|
| Views | 618 | 618 |
| Downloads | 299 | 299 |
| Data volume | 1.1 TB | 1.1 TB |
| Unique views | 536 | 536 |
| Unique downloads | 185 | 185 |
Indexed in
- Publication date:
- February 9, 2021
- DOI:
-
- Keyword(s):
- power system transient stability machine learning deep learning New England test case MATLAB/Simulink PMU signals
- License (for files):
- Creative Commons Attribution 4.0 International