Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy

Zhao, Miaoying; Xu, Gang

doi:10.1049/iet-smt.2017.0188

Cited by 53 publications

(33 citation statements)

References 21 publications

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“…Also, the proposal can detect a fault considering transient and steady states, which is not addressed in other works [11,17,21]. On the other hand, unlike the works presented in [17,21,22,26], the proposal offers an automatic diagnosis that is very important in order to avoid the need of expert users. Finally, the proposal can be considered as a low-complex solution since only three steps are required to (i) process the signal (NMD method), (ii) offer a fault indicator (HT-RMS), and (iii) automate the diagnosis (FLS) by considering different fault severities in transient and steady conditions.…”

Section: Resultsmentioning

confidence: 98%

“…In [18][19][20], the WT is used to decompose vibrations signals, and thus, extract features that allow the fault detection in transformers. Some variations of the WT, i.e., wavelet package transform [21] and empirical wavelet transform [22], are also reported to extract features and diagnose different fault conditions in transformers. It is worth noting that the success of the WT-based methods depends on the proper selection of the mother wavelet and decomposition level [23], which change for different applications.…”

Section: Related Literaturementioning

confidence: 99%

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Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

et al. 2020

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Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis.

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Section: Resultsmentioning

confidence: 98%

Section: Related Literaturementioning

confidence: 99%

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

et al. 2020

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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%

“…Transformer vibration signals are decomposed into several empirical wavelet functions. The signals are calculated to construct the eigenvectors of the transformer vibration signals for classifying three different working conditions (normal conditions, winding axial deformation, and winding radial deformation) [13]. Most papers use wavelets to classify inrush and internal and external faults.…”

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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Application of Improved Symplectic Geometry Mode Decomposition Algorithm in Evaluation System of Transformer Winding Short‐Circuit Withstand Ability

Wang

Guo

2021

IEEJ Transactions Elec Engng

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During the operation of the power transformer, the fault location often occurs in the winding, and the fault phenomenon caused by insufficient short-circuit withstand ability of the winding is particularly prominent. Based on this problem, this article proposes an improved symplectic geometry mode decomposition (ISGMD) algorithm to evaluate the short-circuit withstand ability of winding. The realization method is to use the algorithm to perform time-frequency decomposition of the vibration signal of the transformer, calculate time-domain eigenvalues of the symplectic geometry components to constitute the eigenvector, utilize principal component analysis to reduce dimensionality of the data. Finally, apply the obtained data to train and test the extreme learning machine. The correctness of the algorithm is analyzed by simulating vibration signals. At last, the experiment is designed and the evaluation standard is proposed, and the practicability of the intelligent evaluation model based on the method is verified.

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Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy

Cited by 53 publications

References 21 publications

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

Application of Improved Symplectic Geometry Mode Decomposition Algorithm in Evaluation System of Transformer Winding Short‐Circuit Withstand Ability

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