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Improved Detection Sensitivity with Combined WPT and HHT for Power Transformer Winding Deformation Analysis

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IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
© 2014 by IJCA Journal
ETEIAC
Year of Publication: 2014
Authors:
M. Arivamudhan
S. Santhi
S. Abirami
G. Sugasini

M Arivamudhan, S Santhi, S Abirami and G Sugasini. Article: Improved Detection Sensitivity with Combined WPT and HHT for Power Transformer Winding Deformation Analysis. IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering ETEIAC:5-9, July 2014. Full text available. BibTeX

@article{key:article,
	author = {M. Arivamudhan and S. Santhi and S. Abirami and G. Sugasini},
	title = {Article: Improved Detection Sensitivity with Combined WPT and HHT for Power Transformer Winding Deformation Analysis},
	journal = {IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering},
	year = {2014},
	volume = {ETEIAC},
	pages = {5-9},
	month = {July},
	note = {Full text available}
}

Abstract

The success of health monitoring and condition assessment of power transformers based on winding current signature analysis lies on proper extraction of features. The extraction of features in turn depends on appropriate signal processing methods. Fourier based signal analysis provides only frequency information and also suitable only for stationary signals. In this paper we present a combined Wavelet Packet Transform (WPT) and Hilbert Huang Transform (HHT) based time scale and time frequency analysis for the extraction of power transformer winding current features through an experimental study. The experimental work is based on short circuit test conducted on a 33 kV/11 kV, 10 MVA power transformer and axial winding deformation fault is introduced by loosening the bolts of winding structure. It is observed that Combined WPT and HHT offers better feature extraction strategy than analysis using HHT alone.

References

  • K. Karsai, D. Kerenyi and L. Kiss. (1987). Large power transformers. Elsevier Science Publishers.
  • S. Santhi, S. Jayalalitha, V. Jayashankar, V. Jagadeesh Kumar. (2005). Detection of winding deformations during short time currents tests. Proceedings of IEEE IMTC Ottawa, Canada. May 17-23. 2005.
  • E. Rahimpour, J. Christian, K Feser and H. Mohseni. (2003). Transfer function method to diagnose axial displacement and radial deformation of transformer winding. IEEE Trans. on Power Delivery. vol. 18, No. 2. 493-505.
  • S. A. Ryder. (2003). Diagnosing transformer faults using frequency response analysis. IEEE Electrical Insulation Magazine. Vol. 19. No. 2. 16-22.
  • S. Santhi,V. Jayashankar,V. Jagadeesh Kumar. (2008). Time frequency analysis of method for the detection of winding deformation in transformers during short circuit test. Instrumentation and Measurement Technology Conference proceedings, I2MTC 2008. Canada. 1-5.
  • Weihua Xiong, Ruisong Ji. (2006). Nonlinear Time Series Analysis of Transformer's Core Vibration. IEEE Proceedings of the 6th World Congress on Intelligent Control and Automation. June 21 - 23, Dalian, China. 5493-5496.
  • Arivamudhan M, Santhi S, Abirami S. (2013). Broad band excitation for transformer winding deformation detection and analysis using Hilbert Huang transform. Proceedings of the International conference on Trends in Industrial Measurements and Automation (TIMA-2013). December 2013, Chennai. 79-83.
  • Shuyou Wu, Weiguo Huang, Fanrang Kong, Pugliang Zhe. (2009) Vibration feature extraction of power transformer using an time scale frequency analysis method based on WPT and HHT. Power Electronics and Motion Control Conference. IPEMC'09. 2577-2581.
  • Z. K. Peng, Peter W. Tseb, F. L. Chu. (2005). An improved Hilbert–Huang transform and its application in vibration signal analysis. Journal of Sound and Vibration. 286. 187–205.
  • N. E. Huang, Z. Shen, S. R. Long, et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of Royal society of London, Series A. 903-994.
  • Shuyou Wu, Weiguo Huang, Fanrang Kong, Qiang Wu, Fangming Zhou, Ruifan Zhang, Ziyu Wang. (2010). Extracting Power Transformer Vibration Features by a Time-Scale-Frequency Analysis Method. J. Electromagnetic Analysis and Applications. 2. 31-38.
  • Gao Sheng-Wei, Zhang Mu, Yuan Chen-Hu, Sun Xing-Tao, Zhang Chuang. (2011). Wavelet Packet Analyzing of Power Transformer Partial Discharge Signals. IEEE International Conference on Control, Automation and Systems Engineering (CASE), 30-31 July 2011, Singapore.