Fault Detection and Classification in Transmission Lines Based on Wavelet Transform and ANN

TLDR

The study proposes a novel fault detection and classification method for transmission lines using oscillographic data. The method uses wavelet‑domain analysis of current waveforms to determine fault detection and clearing time, and an artificial neural network to classify faults from voltage and current patterns in the time domain. The approach successfully distinguishes faults from voltage sags and oscillatory transients and achieved excellent performance on real oscillographic data from a Brazilian utility.

Abstract

This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results