Application of generative AI-based data augmentation technique in transformer winding deformation fault diagnosis
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Abstract
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OpenAlex topics: Power Transformer Diagnostics and Insulation · High voltage insulation and dielectric phenomena · Currency Recognition and Detection
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References (54)
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1. FRA interpretation using numerical indices: State-of-the-art10.1016/j.ijepes.2017.01.014 ↗
2. Detection of power transformer winding deformation using improved FRA based on binary morphology and extreme point variation10.1109/TIE.2017.2752135 ↗
3. Turn-to-turn short circuit fault localization in transformer winding via image processing and deep learning method10.1109/TII.2021.3105932 ↗
4. Analysis and interpretation of power auto-transformer winding axial displacement and radial deformation using frequency response analysis10.1016/j.engfailanal.2020.104549 ↗
5. Life estimation of distribution transformers considering axial fatigue in loose winding conductors10.1016/j.engfailanal.2010.09.032 ↗
6. Transformer winding deformation fault diagnosis method based on frequency response binary image
7. Detection of winding faults based on a characterization of the nonlinear dynamics of transformers10.1109/TIM.2018.2838178 ↗
8. Quantitative research on accumulative effect of transformer winding deformation and its influence degree based on time-frequency analysis of vibration signal10.1109/ACCESS.2022.3229524 ↗
9. A novel online technique to detect power transformer winding faults10.1109/TPWRD.2011.2180932 ↗
10. “Research of sweep frequency impedance to determine transformer winding deformation after short-circuit impact,”
11. Development of pulsed method for diagnostics of transformer windings based on short probe impulse10.1109/TDEI.2015.004745 ↗
12. Double-End Excitation of a Single Isolated Transformer Winding: An Improved Frequency Response Analysis for Fault Detection10.1109/TPWRD.2021.3067863 ↗
13. Fault Diagnosis of a Power Transformer Using an Improved Frequency-Response Analysis10.1109/TPWRD.2004.835428 ↗
14. A Bootstrapping solution for effective interpretation of transformer winding frequency response10.1109/TIM.2022.3159012 ↗
15. “IEC/IEEE International Draft Standard - Power Transformers - Part 18: Measurement of frequency response,” IEEE P60076-18_D1, 2012.
16. D. Bormann and P. Picher, “Mechanical condition assessment of transformer windings using frequency response analysis (FRA),” CIGRE Brochure 342, 2008.
17. P. Picher, “Advances in the interpretation of transformer Frequency Response Analysis (FRA),” CIGRE Brochure 812, 2020.
18. “IEEE Guide for the Application and Interpretation of Frequency Response Analysis for Oil-Immersed Transformers,” IEEE Std C57.149-2012, pp. 1-72, 2013.
19. “High Voltage Test Technology Standardization Technical Committee of Electric Power Industry. Frequency response analysis on winding deformation of power transformers,” DL/T 911—2016, 2016.
20. Improved power transformer winding fault detection using FRA diagnostics – part 2: radial deformation simulation10.1109/TDEI.2014.004592 ↗
21. Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation10.1109/TDEI.2014.004591 ↗
22. The actual measurement and analysis of transformer winding deformation fault degrees by FRA using mathematical indicators10.1016/j.epsr.2020.106324 ↗
23. Fault anomaly detection of synchronous machine winding based on isolation forest and impulse frequency response analysis10.1016/j.measurement.2021.110531 ↗
24. Detection of probability of occurrence, type and severity of faults in transformer using frequency response analysis based numerical indices10.1016/j.measurement.2020.108322 ↗
25. Detection of winding faults using image features and binary tree support vector machine for autotransformer10.1109/TTE.2020.2982785 ↗
26. classifying transformer winding deformation fault types and degrees using FRA Based on Support Vector Machine10.1109/ACCESS.2019.2932497 ↗
27. Clustering of transformer condition using frequency response analysis based on k-means and GOA10.1016/j.epsr.2021.107619 ↗
28. Diagnostic of transformer winding deformation fault types using continuous wavelet transform of pulse response10.1016/j.measurement.2019.03.051 ↗
29. Improved Method to Obtain the Online Impulse Frequency Response Signature of a Power Transformer by Multi Scale Complex CWT10.1109/ACCESS.2018.2868058 ↗
30. A New Testing Method for the Diagnosis of Winding Faults in Transformer10.1109/TIM.2020.2998877 ↗
31. A transfer ensemble learning method for evaluating power transformer health conditions with limited measurement data
32. Improved winding mechanical fault type classification methods based on polar plots and multiple support vector machines10.1109/ACCESS.2020.3041298 ↗
33. An Online Data-Driven Technique for the Detection of Transformer Winding Deformations10.1109/TPWRD.2017.2707922 ↗
34. High-frequency resonant overvoltages in transformer regulating winding caused by ground fault initiation on feeding cable10.1109/TPWRD.2017.2700043 ↗
35. ANN and cross-correlation based features for discrimination between electrical and mechanical defects and their localization in transformer winding10.1109/TDEI.2014.004364 ↗
36. Understanding IFRA for Detecting Synchronous Machine Winding Short Circuit Faults Based on Image Classification and Smooth Grad-CAM++10.1109/JSEN.2022.3225210 ↗
37. Improved Interpretation of Impulse Frequency Response Analysis for Synchronous Machine Using Life long Learning Based on iCaRL
38. Transformer Anomaly Detection Method Based on MDS and LOF Algorithm
39. Diagnosing transformer winding deformation faults based on the analysis of binary image obtained from FRA Signature10.1109/ACCESS.2019.2907648 ↗
40. Interpretation of transformer winding deformation fault by the spectral clustering of FRA signature10.1016/j.ijepes.2021.106933 ↗
41. “An attempt of transformer winding fault location based on digital twin,” in
42. Vibration-based fault diagnosis of the natural gas compressor using adaptive stochastic resonance realized by Generative Adversarial Networks10.1016/j.engfailanal.2020.104759 ↗
43. Power system stability assessment method based on GAN and GRU-Attention using incomplete voltage data10.1049/gtd2.12925 ↗
44. A novel assessable data augmentation method for mechanical fault diagnosis under noisy labels10.1016/j.measurement.2022.111114 ↗
45. Generative adversarial nets
46. Wasserstein Generative Adversarial Networks
47. I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. J. A. i. n. i. p. s. Courville, “Improved training of wasserstein gans,” vol. 30, 2017.
48. Conditional Generative Adversarial Nets
49. Unsupervised representation learning with deep convolutional generative adversarial networks
50. Statistical approach for interpretation of power transformers frequency response analysis results10.1049/iet-smt.2014.0097 ↗
51. Improving the numerical indices proposed for the FRA interpretation by including the phase response10.1016/j.ijepes.2016.04.044 ↗
52. A survey on statistical indexes applied on frequency response analysis of electric machinery and a trend based approach for more reliable results10.1016/j.epsr.2016.03.044 ↗
53. P. M. Nirgude, D. Ashokraju, A. D. Rajkumar, and B. P. Singh, “Application of numerical evaluation techniques for interpreting frequency response measurements in power transformers,” IET Science, Measurement & Technology. vol. 2(5) pp. 275-285.10.1049/iet-smt:20070072 ↗
54. “Fault Detection Based on Modified t-SNE”10.1109/SAFEPROCESS45799.2019.9213365 ↗