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Continuous wavelet and Hilbert-Huang Transforms applied for analysis of active and reactive power consumption

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Analysis of power consumption presents a very important issue for power distribution system operators. Some power system processes such as planning, demand forecasting, development, etc.., require a complete understanding of behaviour of power consumption for observed area, which requires appropriate techniques for analysis of available data. In this paper, two different time-frequency techniques are applied for analysis of hourly values of active and reactive power consumption from one real power distribution transformer substation in urban part of Sarajevo city. Using the continuous wavelet transform (CWT) with wavelet power spectrum and global wavelet spectrum some properties of analysed time series are determined. Then, empirical mode decomposition (EMD) and Hilbert-Huang Transform (HHT) are applied for the analyses of the same time series and the results showed that both applied approaches can provide very useful information about the behaviour of power consumption for observed time interval and different period (frequency) bands. Also it can be noticed that the results obtained by global wavelet spectrum and marginal Hilbert spectrum are very similar, thus confirming that both approaches could be used for identification of main properties of active and reactive power consumption time series.
Rocznik
Strony
413--422
Opis fizyczny
Bibliogr. 17 poz., rys., tab., wykr., wzory
Twórcy
autor
  • Department for Development, EPC Elektroprivreda BiH d.d. Sarajevo, Vilsonovo setaliste 15, 71000 Sarajevo, Bosnia and Herzegovina, +387 33 751 742
autor
  • Department for Development, EPC Elektroprivreda BiH d.d. Sarajevo, Vilsonovo setaliste 15, 71000 Sarajevo, Bosnia and Herzegovina, +387 33 751 742
Bibliografia
  • [1] Avdakovic, S., Ademovic, A., Nuhanovic, A. (2012), Insight into the Properties of the UK Power Consumption Using a Linear Regression and Wavelet Transform Approach. Elektrotehniški Vestnih/ Electrotechnical Review, 79, 278-283.
  • [2] Avdakovic, S., Ademovic, A., Nuhanovic, A. (2013), Correlation Between Air Temperature and Electricity Demand by Linear Regression and Wavelet Coherence Approach: UK, Slovakia and Bosnia and Herzegovina Case Study. Archives of Electrical Engineering, accepted for publications, 62.
  • [3] Torrence, C., Compo, GP. (1998), A Practical Guide to Wavelet Analysis, Bulletin of the American Meteorological Society, 79, 61-78.
  • [4] Avdakovic, S., Nuhanovic, A., Kusljugic, M., Becirovic, E., Turkovic, E. (2013), Wavelet Multiscale Analyses of a Power System Load Variance., Turkish Journal of Electrical Engineering & Computer Sciences, 21, 1035-1043.
  • [5] Henley, A., Peirson, J., Non-Linearities in electricity demand and temperature: Parametric Versus Non-Parametric Methods (1997), Oxford Bulletin of Economics and Statistics, 59, 149-162.
  • [6] Parkpoom, S., Harrison, G.P., Bialek, J.W. (2004), Climate Change Impacts on Electricity Demand. In Proc. of the 39th UPEC: 1342-1346.
  • [7] Huang, B.N., Hwang, M.J., Yang, C.W., Causal Relationship between Energy Consumption and GDP Growth Revisited: A Dynamic Panel Data Approach (2008), Ecological economics, 67, 41-54.
  • [8] Ozun, A., Cifter, A., Multi-scale Causality Between Energy Consumption and GNP in Emerging Markets: Evidence From Turkey (2007), Investment Management and Financial Innovations, 4, 60-70.
  • [9] Huang, N., Shen, Z., Long, S., Wu, M., Shih, E., Zheng, Q., Tung, C., Liu, H., (1998), The Empirical Mode Decomposition Method and the Hilbert Spectrum for Non-Stationary Time Series Analysis: Proceedings of the Royal Society of London, A454, 903-995.
  • [10] Huang, N., Wu, M.C., Long, S.R., Shen, S.S.P., Qu, W., Gloersen, P., Fan, K.L. (2003) A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectral Analysis. Proceedings of the Royal Society of London, A459, 2317-2345.
  • [11] Huang, N., Wu, Z., Long, S., Arnold, K., Chen, X., Blank, K., (2009), On Instantaneous Frequency. Advances in Adaptive Data Analysis, 1, 177-229.
  • [12] Huang, N., Wu, Z., A Review on Hilbert-Huang transform: Method and its Applications to Geophysical Studies, Rev. Geophys., 46, RG2006, doi:10.1029/2007RG000228.
  • [13] Flandrin, P., Rilling, G. Goncalves, P. (2004), Empirical Mode Decomposition as a Filter Bank. IEEE Signal Process. Lett., 11, 112-114.
  • [14] Battista, B., Knapp, C., McGee, T., Goebel, V. (2007). Application of the Empirical Mode Decomposition and Hilbert-Huang Transform to Seismic Reflection Data. Geophysics, 72(2), H29-H37, doi: 10.1190/1.2437700.
  • [15] Vincent, C., Gregor, G., Pierre, P., Henrik, M. (2010), Resolving Nonstationary Spectral Information in Wind Speed Time Series Using the Hilbert-Huang Transform. J. Appl. Meteor. Climatol., 49, 253-267. doi: http://dx.doi.org/10.1175/2009JAMC2058.1
  • [16] Messina, A.R., Vittal, V., Heydt, G.T., Browne, T.J. (2009), Nonstationary Approaches to Trend Identification and Denoising Of Measured Power System Oscillations. IEEE Trans. on Power Systems, 24, 1798-1807.
  • [17] Li, H., Zhang, Y., Zheng, H. (2009), Hilbert-Huang Transform and Marginal Spectrum for Detection and Diagnosis of Localized Defects in Roller Bearings. Journal of Mechanical Science and Technology, 23, 291 -301.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-429d4054-7dae-4a2b-9616-d7f1851aafe9
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