Power Transformer Differential Protection Using the Boundary Discrete Wavelet Transform

Medeiros, Rodrigo Prado de; Costa, F. B.; Silva, Kleber M.

doi:10.1109/tpwrd.2015.2513778

Cited by 84 publications

(50 citation statements)

References 30 publications

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“…In addition, wavelets are used to detect faults or abnormalities in transformers [13][14][15][16][17][18][19][20][21][22]. They are used to detect vibrations or electrical signals.…”

Section: Introductionmentioning

confidence: 99%

“…Most papers use wavelets to classify inrush and internal and external faults. The differential current is used as input for the wavelet transform to analyze faults [14][15][16][17][18][19][20][21]. The boundary wavelet transform is implemented in the differential protection of power transformers to distinguish internal faults from other disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…The boundary wavelet transform is implemented in the differential protection of power transformers to distinguish internal faults from other disturbances. The method is designed for real-time applications and implemented in a digital signal processor for real-time analysis [14]. The algorithm distinguishes between internal faults and inrush currents in power transformers.…”

Section: Introductionmentioning

confidence: 99%

“…However, many studies only focus on transmission lines or transformers, and few have combined the two components when analyzing systems. Most of the references use wavelets to detect and identify fault types in electrical systems, but they do not analyze the mother wavelet [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]20,21]. Additionally, there are research articles that compare mother wavelets [18,19,22,23].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

et al. 2020

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This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1–3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

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“…In addition, wavelets are used to detect faults or abnormalities in transformers [13][14][15][16][17][18][19][20][21][22]. They are used to detect vibrations or electrical signals.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

et al. 2020

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“…In contrast to phasor estimation, WT is more accurate with this reason. Medeiros et al [10] proposed transformer protection using boundary discrete WT. Maya et al [11] offered empirical wavelet transform to discriminate internal and external fault under various abnormal conditions.…”

Section: Introductionmentioning

confidence: 99%

Design and development of fault classification algorithm based on relevance vector machine for power transformer

Patel

Chothani

Mistry

et al. 2018

IET Electric Power Applications

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Identification of faults within power transformers is the means of ensuring unit transformer protection. Existing relay maloperates during abnormalities such as magnetising inrush, CT saturation and high resistance internal fault condition. Therefore, it is essential to categorise the internal fault and external abnormality/fault in case of transformer protection. This study presents a new scheme, based on relevance vector machine (RVM) as a fault classifier. The developed algorithm is assessed by simulating various disorders on 345 MVA, 400/220 kV transformer in PSCAD/EMTDC software and also on prototype model with 2 kVA, 230/110 V multi-tapping transformer. One cycle post fault current signals are captured from primary and secondary to form feature vectors. These feature vectors are used as an input to RVM for classification of various test cases. Wide variation in system parameters and fault conditions are considered for test data generation and validation. The proposed scheme is compared with the support vector machine (SVM) and probabilistic neural network (PNN)-based techniques. The proposed scheme successfully discriminates various types of internal faults and external abnormalities in power transformer within a short time. The fault classification accuracy obtained by proposed RVM technique is more than 99% in comparison to SVM and PNN-based schemes.

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Relevance Vector Machine Based Transformer Protection

Patel¹,

Chothani

2020

Power Systems

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Power Transformer Differential Protection Using the Boundary Discrete Wavelet Transform

Cited by 84 publications

References 30 publications

Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

Design and development of fault classification algorithm based on relevance vector machine for power transformer

Relevance Vector Machine Based Transformer Protection

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