Open AccessPrediction Point of Fault Location on Its Campus Power Grid by using Neural Artificial Method
Author(s) -
A. Syamsudin,
Isa Hafidz,
Daeng Rahmatullah,
Dimas Anton Asfani,
I. M. Y Negara
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1096/1/012068
Subject(s) - artificial neural network , fault (geology) , relay , computer science , electric power system , grid , overcurrent , data set , power (physics) , point (geometry) , real time computing , data mining , simulation , reliability engineering , voltage , artificial intelligence , engineering , electrical engineering , physics , geometry , mathematics , quantum mechanics , seismology , geology
The dispersed electric load connected to the power system leads to various nominal current and direction. However, to protect the plan optimally, relay settings must be updated according to its configuration. This paper investigates the prediction of fault location point for Directional Overcurrent Relay (DOCR). The system used Institut Teknologi Sepuluh Nopember (ITS) Campus electricity system connected to the grid utility. Artificial Neural Network (ANN) include data combination of power flow and short circuit as input data, can determine the appropriate fault location of the system. From the result, the data set in the master control has a smaller composition than by performing manual looking for tables. From the simulation result, 313 testing data obtained an average error of 0.002614377 so that the test results are quite close to the target data. Another advantage is that fewer data must be entered in Master control when using ANN, 136 data, compared using a lookup table, 1512 data. Through this method, the user can predict the fault location quickly and accurately.