Quantitative Research on Accumulative Effect of Transformer Winding Deformation and Its Influence Degree Based on Time-Frequency Analysis of Vibration Signal

Gao, Shuguo; Sun, Lei; Wang, Xinyu; Tian, Ye; Geng, Jianghai; Liu, Hongliang

doi:10.1109/access.2022.3229524

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…In the case of the deformation, displacement or collapse of transformer windings, the compression force between wire cakes is insufficient, thus aggravating the unbalance of winding ampere-turns, and the magnetic leakage generated increases the axial force, which aggravates the winding vibration [ 20 ].…”

Section: The Vibration Characteristics Analysis Of Winding End Pad Fa...mentioning

confidence: 99%

Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis

Han

Wang²,

Liao

et al. 2023

Sensors

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A transformer’s acoustic signal contains rich information. The acoustic signal can be divided into a transient acoustic signal and a steady-state acoustic signal under different operating conditions. In this paper, the vibration mechanism is analyzed, and the acoustic feature is mined based on the transformer end pad falling defect to realize defect identification. Firstly, a quality–spring–damping model is established to analyze the vibration modes and development patterns of the defect. Secondly, short-time Fourier transform is applied to the voiceprint signals, and the time–frequency spectrum is compressed and perceived using Mel filter banks. Thirdly, the time-series spectrum entropy feature extraction algorithm is introduced into the stability calculation, and the algorithm is verified by comparing it with simulated experimental samples. Finally, stability calculations are performed on the voiceprint signal data collected from 162 transformers operating in the field, and the stability distribution is statistically analyzed. The time-series spectrum entropy stability warning threshold is given, and the application value of the threshold is demonstrated by comparing it with actual fault cases.

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Section: The Vibration Characteristics Analysis Of Winding End Pad Fa...mentioning

confidence: 99%

Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis

Han

Wang²,

Liao

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Application of generative AI-based data augmentation technique in transformer winding deformation fault diagnosis

Chen,

Zhao,

Liu

et al. 2024

Engineering Failure Analysis

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Research progress on oil-immersed transformer mechanical condition identification based on vibration signals

Sun,

2024

Renewable and Sustainable Energy Reviews

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Quantitative Research on Accumulative Effect of Transformer Winding Deformation and Its Influence Degree Based on Time-Frequency Analysis of Vibration Signal

Cited by 4 publications

References 16 publications

Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis

Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis

Application of generative AI-based data augmentation technique in transformer winding deformation fault diagnosis

Research progress on oil-immersed transformer mechanical condition identification based on vibration signals

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