A Vibration Response Analysis Technique for Condition Monitoring of Transformer Winding
Source abstract
Abstract
Accurate assessment of winding condition for power transformers is critical for ensuring the stable operation of modern power systems. Vibration signal has been regarded as an effective and promising evaluator for winding diagnosis. While on-line vibration monitoring offers the continuous, non-invasive and in-service assessment for winding condition, establishing precise correlations between the variable vibration patterns and specific winding condition remains challenging. To this end, an off-line vibration response analysis (VRA) technique was presented in the paper. Specifically, vibration frequency response (VFR) curves, indicating the winding response, were first obtained when the transformer was excited by the developed vibration response testing system, consisting of constant current variable-frequency power supply, intermediate transformer, accelerometers, data acquisition, control and analysis system. The VFR curves were then quantitatively and comprehensively described through four kinds of correlation indices. Finally, hierarchical integration strategy was proposed to aggregate those indices into quantitative criterion for condition assessment. The proposed method was validated on a real transformer under both normal and fault conditions, demonstrating superior performance. Notably, a 10% decrease in the evaluation criterion indicates an incipient winding looseness, while a reduction of 25% or more suggests severe looseness, prompting timely maintenance recommendations.
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Research context
OpenAlex topics: Power Transformer Diagnostics and Insulation · High voltage insulation and dielectric phenomena · Magnetic Properties and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430019, China
School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
China Yangtze Power Co., Ltd., Yichang 443000, China
Funding
Open Research Fund of Hubei Technology Innovation Center for Smart Hydropower
References (33)
Show source-provided references
1. Impact of high-frequency harmonics (0–9 kHz) generated by grid-connected inverters on distribution transformers10.1016/j.ijepes.2020.106177 ↗
2. Review of generation, identification and suppression of wide-band oscillation in AC-DC power system
3. (2006). Power Transformers—Part 5: Ability to Withstand Short Circuit (Standard No. IEC 60076-5:2006).
4. Frequency response analysis and short-circuit impedance measurement in detection of winding deformation within power transformers10.1109/MEI.2013.6507412 ↗
5. Diagnosis method study of transformer windings deformation basing on sweep frequency of short-circuit impedance10.4028/www.scientific.net/AMM.482.113 ↗
6. Wang, S., Wang, S., Cui, Y., Long, J., Ren, F., Ji, S., and Wang, S. (2020). An experimental study of the sweep frequency impedance method on winding deformation of an onsite power transformer. Energies, 13.10.3390/en13143511 ↗
7. (2012). Power Transformers-Part 18: Measurement of Frequency Response (Standard No. IEC 60076-18:2012).
8. Analysis of statistical methods for assessment of power transformer frequency response measurements10.1109/TPWRD.2020.2987205 ↗
9. Tahir, M., and Tenbohlen, S. (2023). Transformer winding fault classification and condition assessment based on random forest using FRA. Energies, 16.10.3390/en16093714 ↗
10. An attempt to identify the faulty phase in three-phase transformer windings using an advanced FRA measurement technique10.1109/TPWRD.2020.3035361 ↗
11. On analytical estimation of inductances in equivalent ladder network model of a homogeneous transformer winding using FRA data10.1109/TPWRD.2024.3504961 ↗
12. An efficient method to localize and quantify axial displacement in transformer winding using support vector machines
13. Six sigma-based frequency response analysis for power transformer winding deformation10.3390/app15073951 ↗
14. Vibration characteristics of ±800 kV converter transformers part I: Under no-load conditions10.1016/j.ijepes.2024.110060 ↗
15. Vibration characteristics of ±800 kV converter transformers part II: Under load conditions10.1016/j.ijepes.2024.110026 ↗
16. Evaluating power transformer state based on tank vibration: A graphical approach10.1109/TIM.2025.3570351 ↗
17. Theoretical evaluation of transformer short-circuit withstand capability and identification method of winding cumulative deformation10.1109/TPWRD.2025.3575087 ↗
18. Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy10.1049/iet-smt.2017.0188 ↗
19. Application of improved symplectic geometry mode decomposition algorithm in evaluation system of transformer winding short-circuit withstand ability10.1002/tee.23429 ↗
20. Interpretable analysis of transformer winding vibration characteristics: SHAP and multi-classification feature optimization10.1016/j.ijepes.2025.110585 ↗
21. Wireless vibration signal prediction of power transformers based on contrastive adversarial sparse model10.1109/TIE.2023.3294607 ↗
22. Transformer winding fault diagnosis using vibration image and deep learning10.1109/TPWRD.2020.2988820 ↗
23. Vibration signal-based fusion residual attention model for power transformer fault diagnosis10.1109/JSEN.2024.3382811 ↗
24. A mechanical-electromagnetic coupling model of transformer windings and its application in the vibration-based condition monitoring10.1109/TPWRD.2023.3242266 ↗
25. Two-stage zoom FFT-enhanced deep learning-aided weighted scheme for wireless vital sign estimation using mmWave FMCW radar10.1109/LSENS.2024.3416455 ↗
26. Dynamic interpretation of the factors causing dam deformation with hybrid grey dynamic incidence model10.1016/j.engstruct.2021.112482 ↗
27. Multivariate phase space reconstruction and Riemannian manifold for sleep stage classification10.1016/j.bspc.2023.105572 ↗
28. Time-delay identification from chaotic time series via statistical complexity measures based on ordinal pattern transition networks10.1007/s11071-023-09256-2 ↗
29. Echo state network based on improved fruit fly optimization algorithm for chaotic time series prediction10.1007/s12652-020-01920-4 ↗
30. Park, J.-I., and Park, C.-H. (2022). Harmonic contribution assessment based on the random sample consensus and recursive least square methods. Energies, 15.10.3390/en15176448 ↗
31. Li, T., Quan, H., Song, W., Chen, N., Wang, Q., Gao, F., Lin, Y., and Yin, R. (2025, January 19–21). An improved D-S evidence theory-based multi-sensor data fusion method for MIMU. Proceedings of the 2025 5th International Conference on Computer, Internet of Things and Control Engineering, Guangzhou, China.10.1109/CITCE67565.2025.11360028 ↗
32. AHP-CRITIC-TOPSIS-based analysis of the influence of young people’s preferences on the design of Funan wicker home products10.15376/biores.19.4.8216-8237 ↗
33. (2008). Power Transformers—Part 5: Ability to Withstand Short Circuit (Standard No. GB/T 1094.5-2008).