TY - GEN
T1 - ANN-Based modeling of directional overcurrent relay characteristics applied in radial distribution system with distributed generations
AU - Musirikare, Alexandre
AU - Pujiantara, Margo
AU - Tjahjono, Anang
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/13
Y1 - 2018/11/13
N2 - The time multiplier setting (TMS) and the relay pickup current are very important parameters in modeling of DOCR characteristics. These two parameters can be adjusted to move the time current characteristic (TCC) curve to the position suitable to the protection coordination. Distributed generations (DGs) can cause change of fault current in the system and this can affect the protection coordination. In this paper, artificial neural network (ANN) based on Levenberg-Marquardt algorithm is used to model the DOCR characteristics where the trained ANN model is able to compute the time multiplier setting, the pickup current as well as the trip time of each DOCR in terms of changes caused by DG connection. A long radial distribution feeder of 7 buses penetrated by DGs is designed and simulated in order to get the training data set. The training results are quite interesting and encouraging with a mean squared error (mse) equal 1.6766e-l2. At the end, a sample of ANN outputs is implemented in ETAP software for further verification of the developed model and it works perfectly.
AB - The time multiplier setting (TMS) and the relay pickup current are very important parameters in modeling of DOCR characteristics. These two parameters can be adjusted to move the time current characteristic (TCC) curve to the position suitable to the protection coordination. Distributed generations (DGs) can cause change of fault current in the system and this can affect the protection coordination. In this paper, artificial neural network (ANN) based on Levenberg-Marquardt algorithm is used to model the DOCR characteristics where the trained ANN model is able to compute the time multiplier setting, the pickup current as well as the trip time of each DOCR in terms of changes caused by DG connection. A long radial distribution feeder of 7 buses penetrated by DGs is designed and simulated in order to get the training data set. The training results are quite interesting and encouraging with a mean squared error (mse) equal 1.6766e-l2. At the end, a sample of ANN outputs is implemented in ETAP software for further verification of the developed model and it works perfectly.
KW - Artificial neural network (ANN)
KW - Directional overcurrent relay (DOCR)
KW - Distributed generation (DG)
KW - Radial distribution system
UR - http://www.scopus.com/inward/record.url?scp=85058405819&partnerID=8YFLogxK
U2 - 10.1109/ICITEED.2018.8534834
DO - 10.1109/ICITEED.2018.8534834
M3 - Conference contribution
AN - SCOPUS:85058405819
T3 - Proceedings of 2018 10th International Conference on Information Technology and Electrical Engineering: Smart Technology for Better Society, ICITEE 2018
SP - 52
EP - 57
BT - Proceedings of 2018 10th International Conference on Information Technology and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information Technology and Electrical Engineering, ICITEE 2018
Y2 - 24 July 2018 through 26 July 2018
ER -