Ідентифікація зміни ємності DC-DC перетворювача за допомогою вейвлетів

Vladyslav V. Tielieha; Iryna R. Lysiuk; Tetiana Andriivna Khyzhniak

doi:10.20535/2523-4455.2019.24.3.173424

Vol. 24 · Issue 3 · 2019 Jun 28, 2019 Electronic Systems and Signals

Identification of the Capacitance Changes in DC-DC Converter Using Wavelets

Vladyslav V. Tielieha Corresponding Національний технічний університет України "Київський політехнічний інститут імені Ігоря Сікорського" Ukraine

Iryna R. Lysiuk Національний технічний університет України "Київський політехнічний інститут імені Ігоря Сікорського" Ukraine

Tetiana Andriivna Khyzhniak Національний технічний університет України «Київський політехнічний інститут імені Ігоря Сікорського» Ukraine

Pages51-56 PublishedJun 28, 2019

PDF (Ukrainian)

VOL 24 · ISS 3 · 2019 View issue

Abstract

Diagnostics of semiconductor converters is an important step in controlling their state and ensuring their efficiency. The efficiency of the converter depends on the each of its individual elements — failure of one of them leads to an accident. As show the researching, malfunction of the capacitors is the most common cause of the failure of the converter. So, control of its capacity it is an actual task. Analysis of literature show then current and voltage in the converter are essentially depended from the parameters of its elements, so they can be used as diagnostics criteria. But character of this dependence is own for each converter. So, there is task of investigation the different type of semiconductor converters in case of parameters changing. This work oriented to study the buck DC-DC converter in a case when the capacitance of the input capacitor changes in wild range. The model of the converter was created for this purpose in MATLAB ® Simulink ® . Input capacitance changes from 0 to 230 mkF in this model. As shown the modeling, the input capacitor most influences the input current, since the changes in the output current and voltage are corrected by the control system (there is a stabilization of them). So, we consider this current as criteria for identification of the capacitance changes. The set of current diagrams for different values of capacitance was created for analysis how this parameter influences the form of diagrams. This set is the base for the identification of the current state of converter. As show analysis of diagrams, they have big amount of discrete values and there are little differences between them, which very difficult to identify. So, discrete Haar wavelet-transformation was used as the mathematical instrument to reduce the amount of data and obtain information about changes. There are more differences between wavelet coefficients for different capacitance then for current. Because the wavelet coefficients change according to changing of capacitance, they considered as the base for identification. In general, we considered 1024 discrete values of current for which there are 10 levels decomposition of wavelet transformation. The most amount of coefficients is on 10 level — 512 coefficients of approximation and 512 coefficients of detailing. On each next level the amount of coefficients decreases in 2 time. The intervals between minimum and maximum coefficients for different capacitances were calculated for each decomposition level. Results show that this interval the most changes on 8 and 7 levels of decomposition. So, wavelet coefficients on these levels can be used for further diagnostics of converters. The fact that interval of coefficients for some time moment is out of bounds of interval for the nominal value of capacitance is the indicator that capacitance changes. Ref. 14, fig. 5, tabl. 1.

Keywords

#diagnostics #semiconductor converters #wavelet Haar

References

I. F. Domnin, O. V. Sevriukov, and M. R. Verzhanovskaia, “Lokalizatsia neispravnostei silovoi skhemy avtonomnoho invertora toka [Localization of power circuit faults autonomous current inverter],” in Proceeding of Third International Scientific and Technical Conference of Unconventional Electromechanical and Electrical Systems, 1997, vol. 2, pp. 547–552.
Y. S. Petergeria and T. A. Khyzhniak, “Doslidzhennia vplyvu parametriv elementiv kvazyrezonansnoho peretvoriuvacha na veivlet-spectr vyhidnoii napruhy [Investigation of the influence of the element parameters on output voltage wavelet-spectrum in quasi-resonant converter],” Electron. Commun., vol. 5 (40), pp. 33–38, 2007.
J. Gao, Y. Ji, J. Bals, and R. Kennel, “Fault Detection of Power Electronic Circuit using Wavelet Analysis in Modelica,” 2012, pp. 513–522, DOI: 10.3384/ecp12076513.
V. Vinothkumar and D. C. Muniraj, “Fault Diagnosis in H-Bridge Multilevel Inverter Drive Using Wavelet Transforms,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 2, no. 4, pp. 1255–1262, 2013, URL: www.ijareeie.com/upload/april/Fault Diagnosis in H-Bridge Multilevel Inverter Drive Using Wavelet Transforms.html.
T. A. Khyzhniak, T. O. Tereshchenko, M. Y. Ovsiienko, and L. H. Laikova, “Diahnostyka DC-DC peretvoriuvacha z kvazi-impedansnoiu lankoiu [Diagnostics of DC-DC Converters with a Quasi-Impedance Link],” Microsystems, Electron. Acoust., vol. 23, no. 2, pp. 42–48, Apr. 2018, DOI: 10.20535/2523-4455.2018.23.2.133300.
N. Astaf’eva, “Wavelet analysis: basic theory and some applications,” Uspekhi Fiz. Nauk, vol. 166(11), p. 1145, 2008, DOI: 10.3367/UFNr.0166.199611a.1145.
L. A. Zalmanzon, Preobrazovaniya Fur’e, Uolsha, Khaara i ikh primenenie v upravlenii, svyazi i drugikh oblastyakh [Fourier, Walsh, and Haar Transforms and Their Applications in Control, Communication, and Other Fields], Nauka. Moscow, 1989, ISBN: 5020140945.
V. P. Dyakonov, Veyvletyi. Ot teorii k praktike.[Wavelets. From theory to practice], SOLON-Pres. Moskow, 2010.
Y. S. Yamnenko, V. V. Tielieha, and K. S. Niemchinova, “Vikorystannia veivlet-peretvoren Haara ta OB pry analizi syhnaliv [The use of Haar and OB wavelets in the signals analysis],” Electron. Commun., vol. 22, no. 4, pp. 51–58, Aug. 2017, DOI: 10.20535/2312-1807.2017.22.4.105272.
I. M. Dremin, O. V. Ivanov, and V. A. Nechitailo, “Veivlety i ih ispolzovanie [Wavelets and their uses],” Phys. Usp, vol. 171(5), pp. 465–501, 2001, DOI: 10.3367/UFNr.0171.200105a.0465.
V. Y. Zhuykov and Y. S. Tereschenko, T.A. Petergerya, Preobrazovanie diskretnykh sihnalov na konechnikh intervalakh v orientirovannom bazise [Conversion of discrete signals at finite intervals in an oriented basis], Avers. 2004.
T. Khyzhniak and V. Teleha, “Spectralnie preobrazovaniia na konechnom intervale v zadachah diahnostiki [Spectral transformations on a finite interval in diagnostics],” in NDT days 2017, 2017, pp. 105–108, URL: https://www.ndt.net/search/docs.php3?showForm=off&id=21631 .
J.-M. Cheon, C.-K. Kim, J.-M. Lee, S.-J. Kim, and S. Kwon, “A New Fault Detectior for Three-Phase Half-Wave Converter Using Wavelet Transform,” IFAC Proc. Vol., vol. 36, no. 20, pp. 945–950, Sep. 2003, DOI: 10.1016/S1474-6670(17)34595-0.
V. Y. Zhuikov, T. A. Tereshchenko, and Y. S. Peterheria, Simetrichnoe preobrazovanie na konechnykh intervalakh [Symmetric transformation at finite intervals], Avers. Kyiv, 2000.