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Pregled bibliografske jedinice broj: 1110106

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


Sarajcev, Petar; Kunac, Antonijo; Petrovic, Goran; Despalatovic, Marin
Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case, 2021. doi:10.5281/zenodo.4521886 (ostalo).


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

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

Vrsta, podvrsta
Ostale vrste radova, ostalo

Godina
2021

Ključne riječi
power system ; transient stability ; machine learning ; deep learning ; MATLAB ; dataset

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Projekti:
IP-2019-04-7292 - Simulator poremećaja u elektroenergetskom sustavu i kalibrator nesinusnih napona i struja (SIMPES) (Petrović, Goran, HRZZ - 2019-04) ( CroRIS)

Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split

Poveznice na cjeloviti tekst rada:

doi zenodo.org

Citiraj ovu publikaciju:

Sarajcev, Petar; Kunac, Antonijo; Petrovic, Goran; Despalatovic, Marin
Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case, 2021. doi:10.5281/zenodo.4521886 (ostalo).
Sarajcev, P., Kunac, A., Petrovic, G. & Despalatovic, M. (2021) Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case.. Ostalo doi:10.5281/zenodo.4521886.
@unknown{unknown, author = {Sarajcev, Petar and Kunac, Antonijo and Petrovic, Goran and Despalatovic, Marin}, year = {2021}, DOI = {10.5281/zenodo.4521886}, keywords = {power system, transient stability, machine learning, deep learning, MATLAB, dataset}, doi = {10.5281/zenodo.4521886}, title = {Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case}, keyword = {power system, transient stability, machine learning, deep learning, MATLAB, dataset} }
@unknown{unknown, author = {Sarajcev, Petar and Kunac, Antonijo and Petrovic, Goran and Despalatovic, Marin}, year = {2021}, DOI = {10.5281/zenodo.4521886}, keywords = {power system, transient stability, machine learning, deep learning, MATLAB, dataset}, doi = {10.5281/zenodo.4521886}, title = {Power System Transient Stability Assessment Simulations Dataset - IEEE New England 39-bus test case}, keyword = {power system, transient stability, machine learning, deep learning, MATLAB, dataset} }

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