Fault location using wavelet packets

Feng, Yan; Chen, Zhiye; Liang, Zhirui; Kong, Yinghui; Li, Peng

doi:10.1109/icpst.2002.1047251

Cited by 19 publications

(2 citation statements)

References 15 publications

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“…A survey of tests and algorithms being considered by industry is presented here ("IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines" 2015). Some of these methods use fault-originated EMTs, i.e., the traveling waves generated following a fault ((El-Hami et al 1992), (Borghetti et al 2006), (Borghetti et al 2008), (F. Yan et al 2002), (Yan, Ge, and Zhao 2008)). Zhao et.…”

Section: Present State Of the Artmentioning

confidence: 99%

Use of Traveling Wave Signatures in Medium-Voltage Distribution Systems for Fault Detection and Location

Prabakar¹,

Singh²,

Reynolds³

et al. 2021

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Section: Present State Of the Artmentioning

confidence: 99%

Use of Traveling Wave Signatures in Medium-Voltage Distribution Systems for Fault Detection and Location

Prabakar¹,

Singh²,

Reynolds³

et al. 2021

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“…The distance was calculated using power frequency voltage and current measured at the single event recorder. Other authors [3][4][5] made some modifications to the Magnago and Abur proposition, trying new mother wavelets and sampling frequencies to increase accuracy.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

Adv. Electr. Comp. Eng.

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Every power system is vulnerable to fault occurrences. Under permanent fault conditions, maintenance crew has the duty to detect the problem, repair the defect and recover the power supply system. If the fault is previously located, the repair can be performed faster. The common methods to locate the fault in electrical distribution systems use the final user information or some heuristics with fuse coordination and the loss of loads. In this paper, a new algorithm for fault location in distribution power systems is presented. Using computational simulations, travelling waves theory, wavelet transform, a new data preprocessing feature, and artificial neural networks, this new algorithm tries to approximate the fault location using data provided by only one measurement point at the beginning of the feeder.

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