Detection and classification of short-circuit faults on a transmission line using current signal


Çoban M., Tezcan S. S.

Bulletin Of The Polish Academy Of Sciences-Technical Sciences, vol.69, no.4, pp.1-9, 2021 (Journal Indexed in SCI Expanded)

  • Publication Type: Article / Article
  • Volume: 69 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.24425/bpasts.2021.137630
  • Title of Journal : Bulletin Of The Polish Academy Of Sciences-Technical Sciences
  • Page Numbers: pp.1-9

Abstract

This study offers two support vector machine (SVM) models for fault detection and fault classification, respectively. Different short-circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete wavelet transform (DWT) is performed on the measured single terminal current signals before fault detection stage. Three level wavelet energy values obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by a 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi-class SVM classifier. The results of the validation tests have demonstrated that quite a reliable fault detection and classification system can be developed using SVM. The faults generated were used for training and testing of SVM classifiers. An SVM-based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet and fault location were all investigated. Finally, simulation results verified that the study proposed can be used for fault detection and classification on the transmission line.