TY - GEN
T1 - An ANFIS-based fault classification approach in double-circuit transmission line using current samples
AU - Jarrahi, Mohammad Amin
AU - Samet, Haidar
AU - Raayatpisheh, Hossein
AU - Jafari, Ahmad
AU - Rakhshan, Mohsen
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Transmission line protective relaying is an essential feature of a reliable power system operation. Fast detecting, isolating, locating and repairing of the different faults are critical in maintaining a reliable power system operation. On the other hand, classification of the different fault types plays very significant role in digital distance protection of the transmission line. Accurate and fast fault classification can prevent from more damages in the power system. In this paper, an approach is presented to classify the fault in a double-circuit transmission line based on the adaptive Neuro- Fuzzy Inference System (ANFIS) using three phase current samples of only one terminal. This method is independent of effects of variation of fault inception angle, fault location, fault resistance and load angle. MATLAB/Simulink is used to produce fault signals. The proposed method is tested by simulating different scenarios on a given transmission line model. The simulation results denote that the proposed approach for fault identification is able to classify all the faults on the parallel transmission line within half cycle after the inception of fault.
AB - Transmission line protective relaying is an essential feature of a reliable power system operation. Fast detecting, isolating, locating and repairing of the different faults are critical in maintaining a reliable power system operation. On the other hand, classification of the different fault types plays very significant role in digital distance protection of the transmission line. Accurate and fast fault classification can prevent from more damages in the power system. In this paper, an approach is presented to classify the fault in a double-circuit transmission line based on the adaptive Neuro- Fuzzy Inference System (ANFIS) using three phase current samples of only one terminal. This method is independent of effects of variation of fault inception angle, fault location, fault resistance and load angle. MATLAB/Simulink is used to produce fault signals. The proposed method is tested by simulating different scenarios on a given transmission line model. The simulation results denote that the proposed approach for fault identification is able to classify all the faults on the parallel transmission line within half cycle after the inception of fault.
KW - ANFIS
KW - Double-circuit transmission lines
KW - Fault classification
KW - Sugeno fuzzy system
UR - https://www.scopus.com/pages/publications/84937719304
U2 - 10.1007/978-3-319-19222-2_19
DO - 10.1007/978-3-319-19222-2_19
M3 - Conference contribution
AN - SCOPUS:84937719304
SN - 978-3-319-19221-5
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 225
EP - 236
BT - Advances in Computational Intelligence
A2 - Rojas, Ignacio
A2 - Joya, Gonzalo
A2 - Catala, Andreu
PB - Springer
CY - Cham
T2 - 13th International Work-Conference on Artificial Neural Networks, IWANN 2015
Y2 - 10 June 2015 through 12 June 2015
ER -