TY - JOUR
T1 - The modelling of low voltage arc flash based on artificial neural network
AU - Asfani, Dimas Anton
AU - Ilman, Abdillah Fashiha
AU - Sanjaya, Nugroho Wisnu Ari
AU - Negara, I. Made Yulistya
AU - Fahmi, Daniar
AU - Sawitri, Dian Retno
AU - Wahyudi, Mochammad
AU - Al-Azmi, Hadi Lizikri
N1 - Publisher Copyright:
© 2018 ISSN.
PY - 2018/8
Y1 - 2018/8
N2 - This paper dealt with a dynamic modelling of arc flash phenomenon in low voltage installation system based on artificial neural network (ANN). There were two ANN models employed to this proposed model. The first one is dynamic resistance model and the second one is switch or short circuit contact model. The arc flash energy and the number of filaments are defined as the inputs of these ANN models, whereas the targets are the resistance value for dynamic resistance model and the switch value for switch model. The values used in modelling are obtained from experiment of arc flash initiated by phase to neutral short circuit. This fault location is parallel with the resistive load. The feed-forward back-propagation is selected as an algorithm of ANN. The result shows that the proposed model presented the level of accuracy up to 96.7%. In addition, the simulated model revealed that the lower cable impedance is and the higher load is, the greater current peak is and the shorter duration of arc flash is.
AB - This paper dealt with a dynamic modelling of arc flash phenomenon in low voltage installation system based on artificial neural network (ANN). There were two ANN models employed to this proposed model. The first one is dynamic resistance model and the second one is switch or short circuit contact model. The arc flash energy and the number of filaments are defined as the inputs of these ANN models, whereas the targets are the resistance value for dynamic resistance model and the switch value for switch model. The values used in modelling are obtained from experiment of arc flash initiated by phase to neutral short circuit. This fault location is parallel with the resistive load. The feed-forward back-propagation is selected as an algorithm of ANN. The result shows that the proposed model presented the level of accuracy up to 96.7%. In addition, the simulated model revealed that the lower cable impedance is and the higher load is, the greater current peak is and the shorter duration of arc flash is.
KW - Arc flash energy
KW - Dynamic resistance
KW - Feed-forward back-propagation neural network
KW - Low voltage installation system
KW - Parallel arc flash
KW - Phase to neutral fault
UR - http://www.scopus.com/inward/record.url?scp=85050527516&partnerID=8YFLogxK
U2 - 10.24507/ijicic.14.04.1389
DO - 10.24507/ijicic.14.04.1389
M3 - Article
AN - SCOPUS:85050527516
SN - 1349-4198
VL - 14
SP - 1389
EP - 1405
JO - International Journal of Innovative Computing, Information and Control
JF - International Journal of Innovative Computing, Information and Control
IS - 4
ER -