A method of real-time fault diagnosis for power transformers based on vibration analysis

Hong, Keum‐Shik; Huang, Hai; Zhou, Jianping; Shen, Yiming; Li, Yujie

doi:10.1088/0957-0233/26/11/115011

Cited by 47 publications

(24 citation statements)

References 24 publications

(34 reference statements)

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“…On the other hand, the signal processing-based techniques extract features directly from vibration signals in order to perform a fault detection. The most common technique to extract frequency information is the Fourier transform (FT) [10,15,16]. Bartoletti et al [10] propose a set of parameters based on the FT to diagnose the transformer.…”

Section: Related Literaturementioning

confidence: 99%

“…Two metrics, the root mean square (RMS) and the RMS deviation, both obtained from the FT are used to detect mechanical faults in power transformers [15]. Hong et al [16] introduce the frequency complexity analysis using entropy as a measure of frequency uncertainty. Although promising results have been presented, the performance of FT can be compromised when the analyzed signal presents nonstationary events.…”

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: Related Literaturementioning

confidence: 99%

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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“… α , β , γ , and δ in this model represent complex parameters, the values of which are believed to change with the transformer condition . Methods of diagnosis have been proposed that involve conducting wavelet and Hilbert‐Huang transformations on the vibration signal, generating feature extraction vectors, and conducting support vector machine analysis. These diagnosis methods use vibration amplitude as a diagnosis index.…”

Section: Background On Characterizing Vibrations As a Means Of Diagnomentioning

confidence: 99%

Tank Vibration Analysis of an Extra‐High‐Voltage Transformer

Konishi¹,

Κato²,

Hasegawa³

et al. 2020

IEEJ Transactions Elec Engng

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In recent years, measurement of transformer vibration is being carried out as a means of detecting the looseness and deformation of its windings. Conventionally, the vibration components of even multiples of the power supply frequency (electrically generated vibration) were analyzed to diagnose the transformer. On the other hand, the natural vibrational frequencies of the winding structures represent their conditions, and the changes of these frequencies must be representing the changes in their pre‐stress or displacement. We have measured the tank vibration of an extra‐high‐voltage transformer and investigated the dependence of load current, voltage, and temperature of the oil. As a result, although the electrically generated vibration has a complicated dependence, the winding's natural frequency is not affected by the current and voltage, and the influence of the oil temperature is not very large. It is considered that the natural vibrational frequencies of the windings are a suitable index for transformer diagnosis. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Bartoletti et al proposed a low-frequency parameter named the weighted total harmonic distortion to distinguish between normal and anomalous transformers [19]. Similarly, we proposed several signal-based parameters [20], including the frequency complexity analysis based on information entropy and vibration stationarity analysis based on a recurrence plot [21]. It was showed statistically that anomalous transformers have unpredictable high-frequency vibration components.…”

Section: Introductionmentioning

confidence: 97%

Transformer Condition Monitoring Based on Load-Varied Vibration Response and GRU Neural Networks

Hong

Jin

2020

IEEE Access

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Study of the vibration mechanisms of transformer windings may lead to useful applications of vibration techniques to transformer online diagnosis. In a power transformer, the clamping force provides a boundary mechanical constraint to ensure the integrity of the winding. Thus, the looseness of the clamping force is an important health indicator for a transformer. Although the effect of clamping force on a winding's stiffness and natural frequencies is known, the effect of time-varying load current on these natural frequencies remains unsolved. In this paper, this effect is investigated by studying the mechanical frequency response function of an on-load single-phase winding under different clamping forces and variation of the harmonic amplitude of in-service transformers with load current. Then, a gated recurrent unit (GRU) neural network is used to explore the relationship between current sequence and vibration sequence for operating transformers. This study shows that the electromagnetic force induced by the load current affects the vibration response of the winding structure, especially when the looseness of the clamping force is significant. A potential application of the observed phenomenon for online detection of winding conditions is also illustrated. INDEX TERMS dynamic winding model, gated recurrent unit, natural frequency change, transformer vibration.

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A method of real-time fault diagnosis for power transformers based on vibration analysis

Cited by 47 publications

References 24 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

Tank Vibration Analysis of an Extra‐High‐Voltage Transformer

Transformer Condition Monitoring Based on Load-Varied Vibration Response and GRU Neural Networks

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