2014
DOI: 10.1016/j.ijepes.2013.10.003
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High impedance fault location in 11kV underground distribution systems using wavelet transforms

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Cited by 86 publications
(39 citation statements)
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“…Therefore, the new algorithm is insensitive to the normal changes in the power flow of the line. -The algorithm has capability of detecting faulted phase, which supports the single pole tripping and auto-reclosing facility, while, most of previous methods [1][2][3][4][5][6][7][8] are not able to detect the faulted phase. -Due to the simplicity of proposed method, which is just based on WPT, without aid by any other technique such as ANN, fuzzy and intelligent systems, the fault detection part of algorithm is fast.…”
Section: Discussionmentioning
confidence: 99%
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“…Therefore, the new algorithm is insensitive to the normal changes in the power flow of the line. -The algorithm has capability of detecting faulted phase, which supports the single pole tripping and auto-reclosing facility, while, most of previous methods [1][2][3][4][5][6][7][8] are not able to detect the faulted phase. -Due to the simplicity of proposed method, which is just based on WPT, without aid by any other technique such as ANN, fuzzy and intelligent systems, the fault detection part of algorithm is fast.…”
Section: Discussionmentioning
confidence: 99%
“…HIFs on electrical transmission lines include arcing and nonlinear characteristics of fault impedance Therefore, the transients caused by the faults are appropriate features which make HIFs identifiable, using signal processing methods such as Wavelet Transform [2]. The shape of fault induced voltage and current waveforms, include some special features which are used in most of the HIF detection systems [3].…”
Section: Introductionmentioning
confidence: 99%
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“…a j yaklaşım katsayıları ve d j detay katsayıları denklem (2)'deki gibi hesaplanır [21]. (2) ADD analizinde, orijinal sinyal birbirini tamamlayan alçak ve yüksek geçiren iki filtre yoluyla eşit olarak alçak ve yüksek frekanslı bileşenlere ayrılır [22,23]. Alçak geçiren filtre yöntemiyle işlenen sinyalden elde edilen yüksek ölçekli düşük frekanslı yeniden oluşturma bileşenine yaklaşım adı verilmekte ve "A" harfi ile gösterilmektedir.…”
Section: Marmara Fenunclassified
“…The wavelet transform modulus maxima and arriving time of zero and aerial mode components of travelling wave were extracted by wavelet transform to be used as input data for training the SVR to predict the location of the fault. In contrast, the authors in [17] proposed to use a combination of wavelet analysis as the feature extractor together with an ANN and fuzzy logic system to detect the fault types and locations.…”
Section: Introductionmentioning
confidence: 99%