Abstract
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.
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References
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)]
Kovačević J, Chebira A (2007) Life beyond bases: the advent of frames (Part II). IEEE SP Mag. 24(5):115–125 (feature article)
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)
Daubechies I (1992) Ten lectures on wavelets. SIAM, Philadelphia
Mallat S (1997) A wavelet tour of signal processing. Academic Press, New York
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, Australia
Akinci TC, Ekren N, Seker S, Yildirim S (2013) Continuous wavelet transform for ferroresonance phenomena in electric power systems. Electr Power Energy Syst 44:403–409
Ang SP (2010) Ferroresonance simulation studies of transmission systems. Ph.D. Thessis, The University of Manchester, The Faculty of Engineering and Physical Sciences
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)
Postalcioglu OS (2009) Graphical user interface aided online fault diagnosis of electric motor—DC motor case study. Adv Electr Comput Eng 9(3):12–19
Miron A, Chindris MD, Cziker AC (2013) Software tool for real-time power quality analysis. Adv Electr Comput Eng 13(4):125–132
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):79–88
Lajevardi SM, Hussain ZM (2009) Feature extraction for facial expression recognition based on hybrid face regions. Adv Electr Comput Eng 9(3):63–67
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):161–166
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):34–39
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 112:12–19
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 108:43–49
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, Trabzon
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-Spring
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 3800–3803
Vetterli M, Kovačević J (1995) Wavelets and subband coding. Signal processing series. Prentice Hall, Englewood Cliffs
Christensen O (2002) Introduction to Frames and Riesz Bases. Birkhäuser, Cambridge
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Şengüler, T., Şeker, S. Continuous wavelet transform for ferroresonance detection in power systems. Electr Eng 99, 595–600 (2017). https://doi.org/10.1007/s00202-016-0387-0
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DOI: https://doi.org/10.1007/s00202-016-0387-0