Detection of High Impedance Faults using wavelet transform

Abstract

High Impedance Fault (HIF) has long been a challenging problem in network protection. This is because of its difficulty to be detected and discriminated accurately and reliably. In real cases, arcing is usually associated with HIFs. In this paper, arcing currents are produced by setting up a HV experiment. Normal switching transients are also produced by simulation or experiment. They are then analysed based on wavelet transform. It was found by previous researches that db4 is sensitive in detecting high frequency transient. Different features for the fault currents are found and a detection criterion is developed. The simple detection criterion is used to discriminate the high impedance fault from switching transients and normal load current.Index Terms-High impedance fault, wavelet transform, arcing, capacitor switching, magnetizing inrush.I.

Evidential reasoning based approach to high impedance fault detection in power distribution systems

“…For future references, this method is labelled HA1. Wavelet Transform (WT): As mentioned before, the WT is a common tool for extracting desired criteria for pattern recognition and feature extraction. The method proposed by Chen has been considered for the fourth attribute [32]. The natural logarithms of the standard deviation of the second and third detail layers have been set as suitable detection criteria.…”

“…Numerous simulations have shown high accuracy with regard to CBS and fault detection on three‐phase or single‐phase extensions with low‐current amplitudes. Further information on algorithm procedure can be found in [32]. Combinational attempt of harmonic behaviour (STFT): The final attribute has been chosen from a STFT‐based approach proposed by authors in a previously published paper [33].…”

“…Numerous simulations have shown high accuracy with regard to CBS and fault detection on three‐phase or single‐phase extensions with low‐current amplitudes. Further information on algorithm procedure can be found in [32].…”

Real‐time fault analysis of transmission lines using wavelet multi‐resolution analysis based frequency‐domain approach

“…MRA involves filtering of the input signal, which separates the frequency components present in the signal by passing it through a series of low‐pass and high‐pass filters [5–8]. At each level, there are two types of coefficients that are obtained from the low‐pass filter.…”

“…Some efficient frequency‐domain approaches based on wavelet transform (WT) evolved to overcome the above mentioned limitations. WT has good resolution in both time and frequency domain compared with Fourier transform (FT) [4, 5] and short‐time FT [6–10]. The actual implementation of WT is done through multi‐resolution analysis (MRA).…”