Application of Discrete Wavelet Transform for differential protection of power transformers

Oliveira, Mario Orlando; Bretãs, Arturo S.

doi:10.1109/ptc.2009.5282195

Cited by 32 publications

(22 citation statements)

References 16 publications

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“…The down sampling of a time domain signal can be divided into two which are low and high filtering. Down-sampling occurs when the original signal, x(n), that passes through a half band high-pass filter, g(n), detail coefficient and then into a low-pass filter, h(n) (approximation coefficient) (Oliveira and Bretas, 2009). …”

Section: Wavelet Algorithmmentioning

confidence: 99%

Event-Related Potentials Extraction of Working Memory Using Wavelet Algorithm

Tumari

Sudirman²,

Ahmad³

2014

Journal of Computer Science

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This study was designed to classify and determine the Event-Related Potentials (ERPs) signal pattern of normal children on visual response. Thirty-eight children aged between 10 to 12 years old were subjected to a two-phase computer-based assessment while their working memory activity was recorded using a Neurofax-EEG 9200 machine. For children, it is anticipated that some information can be lost when there is too much information given at any one time due to limited memory capacity and this is a type of memory impairment. Based on the visual stimulus responses, EEG signal were recorded and captured from channel location at Fz. This paper explains the extraction of raw EEG signals into grand mean ERPs signal which to determine the pattern of signal developed. The ERPs concerning latency and amplitude variability of the P300 component was evaluated. The analysis was based on Discrete Wavelet Transform (DWT) algorithm and focused on alpha rhythm. Results indicated that the Daubechies wavelet at a decomposition level of 4 (db4) was the most suitable wavelet for pre-processing raw EEG signal of working memory. A significant increase of latency was detected in children aged 10 to 12 years old at channel Fz (frontal midline) when the visual stimuli became more difficult. For amplitude variability, the girls gave higher amplitude at Phase 1. These results supported the concept of increased cognitive memory in children.

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Section: Wavelet Algorithmmentioning

confidence: 99%

Event-Related Potentials Extraction of Working Memory Using Wavelet Algorithm

Tumari

Sudirman²,

Ahmad³

2014

Journal of Computer Science

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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%

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 [4] was proposed a relay based on the principle of harmonic restraint, being able to distinguish between the differential currents due to an internal fault and due to inrush currents, and operating at high speed on the fault current and restricting the operation in the presence of This [5]. This method ensured security for most of the inrush and overexcitation cases but still fail for cases with very low harmonic content in the operating current [6].…”

Section: Introductionmentioning

confidence: 99%

“…In [7] was proposed to use a statistical method of data analysis, the principal component analysis (PCA), based on pattern recognition for the extraction of different characteristics of differential currents. In [5], the discrete wavelet transform (DWT) was employed to extract transient features of transformer threephase differential currents to detect internal fault conditions. In [8] was proposed a wavelet-based technique for monitoring nonstationary variations in order to distinguish between transformer inrush currents and transformer internal faults.…”

Section: Introductionmentioning

confidence: 99%

“…The wavelet coefficients of a signal, as well as their spectral energy, have been used to detect and classify faults and some power quality (PQ) disturbances [11]- [13]. In power transformer protection, some disturbances as external faults, internal faults and transformer energizing, which present transients, can be also properly analyzed by this tool [5], [8], [14] .…”

Section: Introductionmentioning

confidence: 99%

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Differential protection of power transformers using the wavelet transform

Medeiros

Fernandes

2014

2014 IEEE PES General Meeting | Conference &Amp; Exposition

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Several disturbances involving power transformers commonly occur in the Electric Power System. External faults near the transformer, internal faults, as well as transformer energizing and overexcitation are the main events evaluated in the protection of the power transformers. All of the aforementioned events present transients, which can be properly analyzed by the wavelet transform. The Maximal Overlap Discrete Wavelet Transform (MODWT) can be used to detect transients in some power system disturbances. This paper presents an algorithm based on MODWT for detection and identification of external faults near or far the transformer, internal faults, and transformer energization by using differential currents in the wavelet domain. A system of 230 kV was modeled using ATP/EMTP software. The results reveal the advantages of using this type of modeling, since It provides reliability and speed in detection of these events.

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Application of Discrete Wavelet Transform for differential protection of power transformers

Cited by 32 publications

References 16 publications

Event-Related Potentials Extraction of Working Memory Using Wavelet Algorithm

Event-Related Potentials Extraction of Working Memory Using Wavelet Algorithm

Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

Differential protection of power transformers using the wavelet transform

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