Optimization Accuracy of 1P-3P Fault Identification System using DT-ML Algorithm

Optimization Accuracy of 1P-3P Fault Identification System using DT-ML Algorithm
Author(s):
Suresh Aarsey, Ashish Bhargav
Keywords:
Single Phase (1P), Three Phase (3P), Fault Detection, Machine Learning
Abstract
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. Therefore, in this paper machine learning (ML) is used to detect and classified the fault happen in single phase (1P) to ground fault and three phase (3P) to ground fault. Two different tests of each types of fault have been tested in order to prove the effectiveness of ML to detect the fault location by using different length and fault resistance. The simulation has been accomplished in Python with ML fitting tool which build and train the network before evaluated its performance using regression analysis. The analysis shows that the decision tree (DT)-ML 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.
Article Details
Unique Paper ID: 153839 Publication Volume & Issue: Volume 8, Issue 9 Page(s): 131 - 137
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