TY - GEN
T1 - Artificial neural network techniques apply for fault detecting and locating in overhead power transmission line
AU - Pouabe Eboule, Patrick S.
AU - Pretorius, Jan Harm C.
AU - Mbuli, Nhlanhla
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11
Y1 - 2018/11
N2 - The study carried out here in this paper presents the application of artificial intelligence techniques to detect, classify and locate faults on power transmission line very high voltage. This paper compares the results of the techniques call Multi-Layers Perceptron (MLP) and Concurrent Neuro-Fuzzy used to detect, classify and locate the faults on two different very high voltage electrical lines of 735 kV line voltage, 600 km long for the first line and a second line of 400 kV, 120 km long. The advantages of these techniques used are that they permit to increase the level of reliability of a power line, to limit the lost time in the search for the defects when they occur and then to favorize a continuity of service so much sought by companies sign of economic prosperity.
AB - The study carried out here in this paper presents the application of artificial intelligence techniques to detect, classify and locate faults on power transmission line very high voltage. This paper compares the results of the techniques call Multi-Layers Perceptron (MLP) and Concurrent Neuro-Fuzzy used to detect, classify and locate the faults on two different very high voltage electrical lines of 735 kV line voltage, 600 km long for the first line and a second line of 400 kV, 120 km long. The advantages of these techniques used are that they permit to increase the level of reliability of a power line, to limit the lost time in the search for the defects when they occur and then to favorize a continuity of service so much sought by companies sign of economic prosperity.
KW - Artificial Neural Network
KW - Concurrent Neuro Fuzzy
KW - Defuzzification
KW - Faults Classification
KW - Faults Location
KW - Faults detection
KW - Fuzzification
KW - Multi-Layer Perceptron
KW - Power Transmission Lines
UR - http://www.scopus.com/inward/record.url?scp=85069474729&partnerID=8YFLogxK
U2 - 10.1109/AUPEC.2018.8757959
DO - 10.1109/AUPEC.2018.8757959
M3 - Conference contribution
AN - SCOPUS:85069474729
T3 - Australasian Universities Power Engineering Conference, AUPEC 2018
BT - Australasian Universities Power Engineering Conference, AUPEC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Australasian Universities Power Engineering Conference, AUPEC 2018
Y2 - 27 November 2018 through 30 November 2018
ER -