Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Diagnosis of Power Transformer Winding Deformation Based on Centroid Analysis of 3D Frequency Response Curve

Author(s): Xuan Tang* and Zhenhua Li

Volume 15, Issue 7, 2022

Published on: 15 September, 2022

Page: [567 - 578] Pages: 12

DOI: 10.2174/2352096515666220817103825

Price: $65

conference banner
Abstract

Background: As the frequency of transformer winding faults becomes higher and higher, the frequency response analysis used to detect the winding status has attracted more and more attention. At present, there is still a lack of reliable and intelligent technologies for detecting the state of transformer windings in this field.

Methods: In this paper, a transformer winding deformation diagnosis method based on centroid analysis of 3D frequency response curve is proposed. A 3D coordinate system is established with frequency, amplitude, and phase as the x, y, and z, respectively. The proposed method calculates the centroid of each 3D frequency response curve through an algorithm, and establishes a 3D coordinate system with the centroid of the normal winding 3D frequency response curve as the origin. The deviation distance and deviation angle between the centroid of mass of the 3D frequency response curve of the faulty winding in different frequency bands and the centroid of the 3D frequency response curve of the normal winding and the correlation coefficient of the traditional Frequency Response Analysis (FRA) curve. are calculated. The deviation distance and angle of the centroid and the correlation coefficient of the calculated frequency band are input as features into the Support Vector Machine (SVM) for intelligent diagnosis to achieve fault classification and optimizing its parameters with three optimization algorithms respectively.

Results: The fault classification is realized by analyzing the distribution intervals of the centroids of the 3D frequency response curves of the three simulated faults. The accuracy of fault diagnosis using the 3D frequency response curve feature is significantly higher than the accuracy when using the traditional FRA curve feature and it proves the effectiveness of the proposed method.

Conclusion: The proposed model can effectively identify different fault types and the diagnostic rate of the fault type is 100%.

Keywords: 3D frequency, response curve, centroid, winding fault, SVM, centroid shift

« Previous Next »
Graphical Abstract

[1]
H. Weng, H. Chen, and Y. Wan, "A novel criterion to distinguish inrush current from fault current based on the Bhattacharyya coefficient", Power Sys. Protect. Control, vol. 48, no. 10, pp. 113-122, 2020.
[2]
S. Krishnamurthy, and B.E. Elenga Baningobera, "“IEC61850 standard-based harmonic blocking scheme for power transformers”, Protect", Control Modern Power Sys., vol. 4, no. 2, pp. 121-135, 2019.
[http://dx.doi.org/10.1186/s41601-019-0123-7]
[3]
X. He, Y. Zhang, and G. Cui, "Research on transformer fault detection method based on a regression algorithm", Power Sys. Protect. Control, vol. 48, no. 21, pp. 132-139, 2020.
[4]
T. Fang, Y. Qian, and C. Guo, "Research on transformer fault diagnosis based on a beetle antennae search optimized support vector machine", Power Sys. Protect. Control, vol. 48, no. 20, pp. 90-96, 2020.
[5]
Z.H. Li, Y. Zhang, and W. Yao, "Transformer winding micro deformation classification method based on sweep frequency impedance method and support vector machine In:", Research Gate. vol. 58, no. 1, pp. 99, 2019.
[6]
Q. Yang, "Application status of power transformer winding state detection technology", Telecom Power Technol., vol. 36, no. 02, pp. 138-139, 2019.
[7]
X. Sun, W. He, and J. Zhan, "Current status and development of test and diagnostic technique of transformer winding deformation", High Volt. Engin., vol. 42, no. 04, pp. 1207-1220, 2016.
[8]
A.A. Adly, "Computation of inrush current forces on transformer windings", IEEE Trans. Magn., vol. 37, no. 04, pp. 2855-2857, 2001.
[http://dx.doi.org/10.1109/20.951327]
[9]
E.P. Dick, and C.C. Erven, "Transformer diagnostic testing by frequency response analysis", IEEE Trans. Power Apparatus Syst, vol. 97, no. 6, pp. 2144-2153, 1978.
[http://dx.doi.org/10.1109/TPAS.1978.354718]
[10]
M. Zhu, Z. Jin, and Z. Zhu, "Parameter identification of transformer equivalent model in the diagnosis of winding deformation", Dianli Xitong Zidonghua, no. 08, pp. 38-41, 2001.
[11]
D. Xu, and Y. Li, "Application of artificial neural network to the detection of the transformer winding deformation", High Volt. Engin, no. 03, pp. 21-23, 1998.
[12]
V. Behjat, A. Vahedi, A. Setayeshmehr, H. Borsi, and E. Gockenbach, "Diagnosing shorted turns on the windings of power transformers based upon online FRA using capacitive and inductive couplings", IEEE Trans. Power Deliv., vol. 26, no. 4, pp. 2123-2133, 2011.
[http://dx.doi.org/10.1109/TPWRD.2011.2151285]
[13]
Y. Liu, K. Gao, and Y. Xu, "Feature difference index analysis on frequency response fingerprint of transformer windings", Power Sys. Technol., vol. 34, no. 09, pp. 144-148, 2010.
[14]
Y. Liu, H. Cheng, and H. Hu, "Method for classification of transformer winding deformation based on complex values of the frequency response and digital image processing technology", High Volt. Engin. vol. 45, no. 03, pp. 907-913
[15]
M. Bagheri, M.S. Naderi, T. Blackburn, and T. Phung, "Frequency response analysis and short circuit impedance measurement in detection of winding deformation within power transformers", IEEE Elec. Insul. Mag., vol. 29, no. 3, pp. 33-40, 2013.
[http://dx.doi.org/10.1109/MEI.2013.6507412]
[16]
Z. Zhao, C. Yao, and C. Li, "Method for obtaining the impulse frequency response curves of power transformer winding deformation based on short time fourier transform", High Volt. Engin., vol. 42, no. 01, pp. 241-247, 2016.
[17]
Z. Zhao, C. Tang, and C. Li, "Transformer winding deformation fault diagnosis method based on frequency response binary image", High Volt. Technol., vol. 45, no. 05, pp. 1526-1534, 2019.
[18]
M. Bagheri, M.S. Naderi, and T. Blackburn, "Advanced transformer winding deformation diagnosis: Moving from off-line to on-line", IEEE Trans. Dielectr. Electr. Insul., vol. 19, no. 6, pp. 1860-1870, 2012.
[http://dx.doi.org/10.1109/TDEI.2012.6396941]
[19]
Q. Fu, W. Ning, and D. Liu, "Analysis method for mixed faults in winding based on multi-index fusion", High Volt. Technol., vol. 47, no. 02, pp. 537-545, 2021.
[20]
A. Siada, I. Radwan, and A.F. Abdou, "3D approach for fault identification within power transformers using frequency response analysis", IET Sci. Measur. Technol., vol. 13, no. 6, pp. 903-911, 2019.
[http://dx.doi.org/10.1049/iet-smt.2018.5573]

Rights & Permissions Print Cite
Article Metrics
1
© 2024 Bentham Science Publishers | Privacy Policy