Open AccessFault detection and classification in three phase series compensated transmission line using ANN
Author(s) -
N. Rosle,
Noor Fazliana Fadzail,
Muhammad Idham Abdul Halim,
Mohamad Nur Khairul Hafizi Rohani,
M. I. Fahmi,
W.Z. Leow,
Nur Najihah Abu Bakar
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1432/1/012013
Subject(s) - fault (geology) , electric power transmission , artificial neural network , stuck at fault , transmission line , fault indicator , matlab , computer science , line (geometry) , fault coverage , fault detection and isolation , compensation (psychology) , series (stratigraphy) , engineering , control theory (sociology) , reliability engineering , electronic engineering , artificial intelligence , electronic circuit , electrical engineering , mathematics , telecommunications , psychology , paleontology , geometry , control (management) , seismology , geology , psychoanalysis , actuator , biology , operating system
Series compensation consists of capacitors in series is used in the transmission lines as a tool to improve the performance after disturbed by a fault. Transmission line needs a protection scheme to protect the lines from faults due to natural disturbances, short circuit and open circuit faults. The fault can happen in any location of transmission line and it is important to know which location has been affected. So that, the fault can be eliminated and can maintain the optimum performance. Therefore, in this paper Artificial Neural Network (ANN) is used to detect and classified the fault happen in single line to ground fault and three phase to ground fault. Two different tests of each types of fault have been tested in order to prove the effectiveness of ANN to detect the fault location by using different length and fault resistance. The simulation has been accomplished in MATLAB with ANN fitting tool which build and train the network before evaluated its performance using regression analysis. The analysis shows that the ANN can accurately detect the different types of faults and classified it into the respective category even the random vectors are put on the system are used.