Tank Vibration Analysis of an Extra‐High‐Voltage Transformer

Konishi, Yukiko; Κato, Masamichi; Hasegawa, Masayuki; Aoyama, Hikaru; Onuki, Motohiko; Matsumoto, Satoshi

doi:10.1002/tee.23117

Cited by 9 publications

(4 citation statements)

References 14 publications

(13 reference statements)

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“…Zhou et al proposed a winding vibration model in combination with electromagnetic analysis and obtained stable vibration distribution in the axial direction [14] . Konishi et al measured the vibration of an oil tanker in an ultra-high-voltage transformer and explored the correlation of load current and voltage with oil temperature [15] . Zhang et al investigated transformers' vibration source and transmission characteristics, which are significant in explaining the constitution of oil tanker vibration [16] .…”

Section: Introductionmentioning

confidence: 99%

Design of the porous rubber vibration damper based on the vibration characteristics of the power transformer and applications to noise reduction

Li,

Cai,

Xie

et al. 2023

International Conference on Precision Instruments and Optical Engineering (PIOE 2023)

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Section: Introductionmentioning

confidence: 99%

Design of the porous rubber vibration damper based on the vibration characteristics of the power transformer and applications to noise reduction

Li,

Cai,

Xie

et al. 2023

International Conference on Precision Instruments and Optical Engineering (PIOE 2023)

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“…The recent research shows that the vibration of transformers is strongly related to the mechanical properties of windings [2]. Therefore, more attentions have been paid to transformers vibration signal analysis [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Shang

et al. 2021

Entropy

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Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

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“…e vibration of the extra-high-voltage transformer tank was measured, and the load current, voltage, and oil temperature were investigated. It was pointed out that the natural vibration frequency of the winding is a more suitable diagnostic index [16]. Hong et al analyzed the influence of load current on winding vibration response and proposed a transformer condition monitoring method based on gated recurrent unit neural network.…”

Section: Introductionmentioning

confidence: 99%

State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA‐BP Neural Network

Song

Xiao

et al. 2021

Shock and Vibration

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Vibration analysis is considered as an effective and reliable nondestructive technique for monitoring the operation conditions of elevator control transformer. In the paper, a novel model using the Empirical Mode Decomposition (EMD), the empirical wavelet packet transform, the mind evolutionary algorithm (MEA), and the backpropagation (BP) neural network is proposed for elevator control transformer fault diagnosis. Firstly, the collected signal is smoothed by EMD, the intrinsic mode function (IMF) components with large noise are determined according to the correlation coefficient, the wavelet adaptive threshold denoising algorithm is used to process the noisy IMF components, and the IMF components before and after processing and its residual component are reconstructed to obtain the denoised signal. Then, the denoised signal is transformed by empirical wavelet packet transform to extract the energy ratio and energy entropy features in the wavelet packet coefficients. Finally, a fault diagnosis model composed of MEA and BP neural network is developed, which avoids the problems of premature convergence and poor diagnosis effect. The experimental results show that the proposed model has a remarkable performance with an average root mean square error of 0.00672 and the average diagnosis accuracy of 90.8%, which is better than classic BP neural network.

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Tank Vibration Analysis of an Extra‐High‐Voltage Transformer

Cited by 9 publications

References 14 publications

Design of the porous rubber vibration damper based on the vibration characteristics of the power transformer and applications to noise reduction

Design of the porous rubber vibration damper based on the vibration characteristics of the power transformer and applications to noise reduction

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA‐BP Neural Network

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