Applications of discrete wavelet transform for transformer inrush current detection in protective control scheme

Kunakorn, A.

doi:10.1109/iscit.2004.1413843

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…From the simulated signals, the differential currents, which are the difference between the primary current and the secondary current in all three phases, and the zero sequence are calculated, and the resulting current signals are employed to decompose the high-frequency (details) and low-frequency (approximations) components from the simulated current signals using the mother wavelet daubechies4 (db4) [18][19][20]. From our previous paper, the coefficient details of the resulting current signals obtained from the DWT are squared.…”

Section: Decision Algorithm and Resultsmentioning

confidence: 99%

“…The idea of application of wavelet transform to fault diagnosis is not new, and there are a number of research papers related to this idea 11–27. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…[37], an online sweep frequency-response analysis (SFRA) was developed to detect winding interturn faults of power transformers in service using the transfer function method. The idea of application of wavelet transform to fault diagnosis is not new, and there are a number of research papers related to this idea [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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A discrete wavelet transform approach to discriminating among inrush current, external fault, and internal fault in power transformer using low-frequency components differential current only

Ngaopitakkul

Jettanasen

2014

IEEJ Trans Elec Electron Eng

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This paper proposes an algorithm based on discrete wavelet transform (DWT) for discriminating among inrush current, internal fault, and external fault in power transformers. Fault conditions are simulated using the Alternative Transients Program/Electromagnetic Transients Program (ATP/EMTP). Daubechies4 (db4) is employed as the mother wavelet to decompose low‐frequency components from fault signals. The ratio between per unit (p.u.) differential current and p.u. time is suggested as an index. The numerator of the ratio is the difference between the maximum differential current and the minimum differential current in terms of p.u. with a base value selected at the transformer‐rated current. The ratio is calculated for all three phases, and from a trial and error process the indices for the separation among the internal fault condition, the external fault condition, and inrush condition are defined. The results obtained from the proposed technique show good accuracy for discriminating faults in the considered system. In addition, the proposed algorithm uses data of the differential current with a time of quarter cycle under the analysis. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Decision Algorithm and Resultsmentioning

confidence: 99%

“…The idea of application of wavelet transform to fault diagnosis is not new, and there are a number of research papers related to this idea 11–27. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A discrete wavelet transform approach to discriminating among inrush current, external fault, and internal fault in power transformer using low-frequency components differential current only

Ngaopitakkul

Jettanasen

2014

IEEJ Trans Elec Electron Eng

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“…The idea of application of wavelet transform to fault diagnosis is not new, and there is a number of research papers related to this idea [9][10][11][12][13][14]. The advantage of the wavelet transform is that the band of analysis can be fine adjusted so that high frequency components and low frequency components are detected precisely.…”

Section: Introductionmentioning

confidence: 99%

“…Results from the wavelet transform are shown both in time domain and in frequency domain. In previous research works [12], an analysis of the spectrum of the transient current signal is performed in order to determine whether the current is a fault or a magnetizing inrush current. The approximated signal of DWT is then employed in the algorithm for a decision unit in the protection scheme.…”

Section: Introductionmentioning

confidence: 99%

The Spectrum Comparison Technique of DWT for Discriminating between External Fault and Internal Fault in Power Transformer

Jettanasen

Ngaopitakkul²

2012

Journal of International Council on Electrical Engineering

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-This paper proposes a technique for detecting and identifying internal winding fault of threephase two-winding transformer. A spectrum component obtained from DWT of differential current is analyzed. A ratio between per unit differential current and per unit time is calculated and performed as comparison indicator in order to discriminate between internal fault condition and external fault condition. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. Results show that the proposed technique has good accuracy to detect fault and to identify its position in the considered system.

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A Hybrid Wavelet--ANN Approach in Transformer Protection

Panda

Garlapti

Konar

et al. 2010

2010 International Conference on Advances in Recent Technologies in Communication and Computing

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Applications of discrete wavelet transform for transformer inrush current detection in protective control scheme

Cited by 5 publications

References 6 publications

A discrete wavelet transform approach to discriminating among inrush current, external fault, and internal fault in power transformer using low-frequency components differential current only

A discrete wavelet transform approach to discriminating among inrush current, external fault, and internal fault in power transformer using low-frequency components differential current only

The Spectrum Comparison Technique of DWT for Discriminating between External Fault and Internal Fault in Power Transformer

A Hybrid Wavelet--ANN Approach in Transformer Protection

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