Electrical Engineering

, Volume 99, Issue 2, pp 595–600 | Cite as

Continuous wavelet transform for ferroresonance detection in power systems

  • Tayfun ŞengülerEmail author
  • Serhat Şeker
Original Paper


Wavelet transforms, such as Continuous Wavelet Transform (CWT) create numerous signals based on scale coefficients after decomposition. This entire signal set and their subsets with regard to a chosen function can actually be used to reconstruct redundant representations of the real signal in such a manner that different signal characteristics like anomalies can be detected. This study aims to provide another perspective to wavelet transform studies in terms of the redundancy feature which comes from wavelet type and scale number as well as transform type itself. Consequently, a partial feature construction (PFC) method has been introduced to select the appropriate signal subset which will reconstruct redundant signals for anomaly detection. As a real-world application, ferroresonance data generated by a MATLAB-Simulink model have been used. PFC method has been applied to a subset of decomposed ferroresonance data from CWT, and it has been investigated with different function scenarios. As a result, redundant signals reconstructed by specific functions in PFC have not only allowed detecting ferroresonance phenomenon, but they also made it clearer to observe physical ferroresonance behavior.


Continuous wavelet transforms Redundancy Ferroresonance Feature extraction Reconstruction algorithms 


  1. 1.
    Kovačević J, Chebira A (2007) Life beyond bases: The advent of frames (Part I). IEEE SP Mag. 24(5):115–125. doi: 10.1109/MSP.2007.4286567 [feature article (Online)]
  2. 2.
    Kovačević J, Chebira A (2007) Life beyond bases: the advent of frames (Part II). IEEE SP Mag. 24(5):115–125 (feature article)Google Scholar
  3. 3.
    Misiti M, Misiti Y, Oppenheim G, Poggi J (2010) “MATLAB Wavelet Toolbox\(^{{\rm TM}}\) 4, Getting Started Guide”, Revised for version 4.6 (release 2010b), pp 1–8, 1–9 (online only)Google Scholar
  4. 4.
    Daubechies I (1992) Ten lectures on wavelets. SIAM, PhiladelphiaCrossRefzbMATHGoogle Scholar
  5. 5.
    Mallat S (1997) A wavelet tour of signal processing. Academic Press, New YorkzbMATHGoogle Scholar
  6. 6.
    Mokryani G, Haghifam MR, Esmaeilpoor J (2007) Identification of Ferroresonance Based On Wavelet Transform and Artificial Neural Networks. In: IEEE Power Engineering Conference. AUPEC, Australasian Universities, AustraliaGoogle Scholar
  7. 7.
    Akinci TC, Ekren N, Seker S, Yildirim S (2013) Continuous wavelet transform for ferroresonance phenomena in electric power systems. Electr Power Energy Syst 44:403–409CrossRefGoogle Scholar
  8. 8.
    Ang SP (2010) Ferroresonance simulation studies of transmission systems. Ph.D. Thessis, The University of Manchester, The Faculty of Engineering and Physical SciencesGoogle Scholar
  9. 9.
    Akinci TC, Seker S, Ekren N (2009) Spectral analysis for signal based on ferroresonance phenomena in electric power system. J Tech Univ Sofia Branch Plovdiv Fund Sci Appl 14(1):239–244 (ISSN:1310–271)Google Scholar
  10. 10.
    Postalcioglu OS (2009) Graphical user interface aided online fault diagnosis of electric motor—DC motor case study. Adv Electr Comput Eng 9(3):12–19CrossRefGoogle Scholar
  11. 11.
    Miron A, Chindris MD, Cziker AC (2013) Software tool for real-time power quality analysis. Adv Electr Comput Eng 13(4):125–132CrossRefGoogle Scholar
  12. 12.
    Aydin I, Karakose M, Akin E (2014) A new contactless fault diagnosis approach for pantograph-catenary system using pattern recognition and image processing methods. Adv Electr Comput Eng 14(3):79–88CrossRefGoogle Scholar
  13. 13.
    Lajevardi SM, Hussain ZM (2009) Feature extraction for facial expression recognition based on hybrid face regions. Adv Electr Comput Eng 9(3):63–67CrossRefGoogle Scholar
  14. 14.
    Ushenko YO, Tomka YY, Marchuk YI, Balanetcka VO (2010) Statistical and fractal processing of phase images of human biological fluids. Adv Electr Comput Eng 10(4):161–166CrossRefGoogle Scholar
  15. 15.
    Senguler T, Karatoprak E, Seker S (2010) A new MLP approach for the detection of the incipient bearing damage. Adv Electr Comput Eng 10(3):34–39CrossRefGoogle Scholar
  16. 16.
    Venkatesh C, Shanti Swarup K (2014) Performance assessment of distance protection fed by capacitor voltage transformer with electronic ferro-resonance suppression circuit. Elsevier Electric Power Syst Res 112:12–19CrossRefGoogle Scholar
  17. 17.
    Ugura M, Ceklib S, Uzunoglua CP (2014) Amplitude and frequency detection of power system signals with chaotic distortions using independent component analysis. Elsevier Electric Power Syst Res 108:43–49CrossRefGoogle Scholar
  18. 18.
    Beheshtaein S (2012) Application of wavelet-base method and DT in detection of ferroresonance from other transient phenomena. In: International symposium on innovations in intelligent systems and applications (INISTA). IEEE, TrabzonGoogle Scholar
  19. 19.
    Mokryani G, Haghifam MR (2009) Application of wavelet transform and MLP neural network for ferroresonance identification. Iran J Electr Comput Eng 8(1):9–15 Winter-SpringGoogle Scholar
  20. 20.
    Guo PY, Li CZ, Yan S, Liangcheng J, Zhang JY (2011) Application of wavelet transform in ferroresonance detection. In: Institute of Electrical and Electronics Engineers, Proc. of The 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, pp 3800–3803Google Scholar
  21. 21.
    Vetterli M, Kovačević J (1995) Wavelets and subband coding. Signal processing series. Prentice Hall, Englewood CliffsGoogle Scholar
  22. 22.
    Christensen O (2002) Introduction to Frames and Riesz Bases. Birkhäuser, CambridgezbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Faculty of Electrical and Electronic Engineeringİstanbul Technical University (İTÜ)IstanbulTurkey

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