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Tytuł artykułu

An application of continuous wavelet transform and wavelet coherence for residential power consumer load profiles analysis

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Load profiles of residential consumers are very diverse. This paper proposes the usage of a continuous wavelet transform and wavelet coherence to perform analysis of residential power consumer load profiles. The importance of load profiles in power engineering and common shapes of profiles along with the factors that cause them are described. The continuous wavelet transform and wavelet coherence has been presented. In contrast with other studies, this research has been conducted using detailed (not averaged) load profiles. Presented load profiles were measured separately on working day and weekend during winter in two urban households. Results of applying the continuous wavelet transform for load profiles analysis are presented as coloured scalograms. Moreover, the wavelet coherence was used to detect potential relationships between two consumers in power usage patterns. Results of coherence analysis are also presented in a colourful plots. The conducted studies show that the Morlet wavelet is slightly better suitable for load profiles analysis than the Meyer’s wavelet. Research of this type may be valuable for a power system operator and companies selling electricity in order to match their offer to customers better or for people managing electricity consumption in buildings.
Rocznik
Strony
art. no. e136216
Opis fizyczny
Bibliogr. 23 poz., rys.
Twórcy
autor
  • Warsaw University of Technology, Faculty of Electrical Engineering, Power Engineering Institute, ul. Koszykowa 75, 00-662, Warsaw, Poland
Bibliografia
  • [1] M. Bicego, A. Farinelli, E. Grosso, D. Paolini, and S.D. Ramchurn, “On the distinctiveness of the electricity load profile”, Pattern Recognit. 74, 317‒325 (2018), doi: 10.1016/j.patcog.2017.09.039
  • [2] P. Piotrowski, D. Baczyński, S. Robak, M. Kopyt, M. Piekarz, and M. Polewaczyk, “Comprehensive forecast of electromobility mid-term development in Poland and its impacts on power system demand”, Bull. Pol. Ac.: Tech, 68(4), 697‒709 (2020), doi: 10.24425/bpasts.2020.134180
  • [3] M. Sepehr, R. Eghtedaei, A. Toolabimoghadam, Y. Noorollahi, and M. Mohammadi, “Modeling the electrical energy consumption profile for residential buildings in Iran”, Sustain. Cities Soc. 41, 481‒489 (2018), doi: 10.1016/j.scs.2018.05.041
  • [4] Z. Ning and D. Kirschen, “Preliminary Analisys of High Resolution Domestic Load Data, Part of Supergen Flexnet Project”, The University of Manchester, 2010. [Online]. https://labs.ece. uw.edu/real/Library/Reports/Preliminary_Analysis_of_High_ Resolution_Domestic_Load_Data_Compact.pdf
  • [5] J.L. Ramirez-Mendiola, Ph. Grunewald, and N. Eyre, “Linking intra-day variations in residential electricity demand loads to consumer’s activities: What’s missing ?”, Energy Build. 161, 63‒71 (2018), doi: 10.1016/j.enbuild.2017.12.012
  • [6] J.L. Ramirez-Mendiola, Ph. Grunewald, and N. Eyre, “The diversity of residential electricity demand – A comparative analysis of metered and simulated data”, Energy Build. 151, 121‒131 (2017), doi: 10.1016/j.enbuild.2017.06.006
  • [7] M. Bartecka, P. Terlikowski, M. Kłos, and Ł. Michalski, „Sizing of prosumer hybrid renewable energy systems in Polnad”, Bull. Pol. Ac.: Tech, 68(4), 721‒731 (2020), doi: 10.24425/ bpasts.2020.133125
  • [8] D.S. Osipov, A.G. Lyutarevich, R.A. Gapirov, V.N. Gorunkov, and A.A. Bubenchikov, “Applications of Wavelet Transform for Analysis of Electrical Transients in Power Systems: The Review”, Prz. Elektrotechniczny (Electrical Review), 92(4), 162‒165 (2016), doi: 10.15199/48.2016.04.35
  • [9] R. Kumar and H.O. Bansal, “Hardware in the loop implementation of wavelet based strategy in shunt active power filter to mitigate power quality issues”, Electr. Power Syst. Res. 169, 92‒104 (2019), doi: 10.1016/j.epsr.2019.01.001
  • [10] R. Escudero, J. Noel, J. Elizondo, and J. Kirtley, “Microgrid fault detection based on wavelet transformation and Park’s vector approach”, Electr. Power Syst. Res. 152, 401‒410 (2017), doi: 10.1016/j.epsr.2017.07.028
  • [11] M. El-Hendawi and Z. Wang, “An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting”, Electr. Power Syst. Res. 182 (2020), doi: 10.1016/j.epsr.2020.106265
  • [12] K. Dowalla, W. Winiecki, R. Łukaszewski, and R. Kowalik, „Electrical appliances identyfication based on wavelet transform of power supply voltage signal”, Prz. Elektrotechniczny (Electrical Review), 94 (11), 43‒46 (2018), doi: 10.15199/48.2018.11.10 [in Polish].
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  • [15] P. Sleziak, K. Hlavcova, and J. Szolgay, “Advanatges of a time series analysis using wavelet transform as compared with Fourier analysis”, Slov. J. Civ. Eng. 23(2), 30‒36, (2015), doi: 10.1515/ sjce-2015-0010
  • [16] S. Avdakovic, A. Nuhanovic, M. Kusljugic, E. Becirovic and E. Turkovic, “Wavelet multiscale analysis of a power system load variance”, Turk. J. Electr. Eng. Comp. Sci. 1035‒1043, (2013), doi: 10.3906/elk-1109-47
  • [17] M. Hayn, V. Bertsch, and W. Fichtner, “Electricity load profiles in Europe: The importance of household segmentation”, Energy Res. Soc. Sci. 3, 30–45, (2014), doi: 10.1016/j.erss.2014.07.002
  • [18] R. Cruickshank, G. Henze, R. Balaji, H. Br-Mathias, and A. Florita, “Quantifying the Opporturnity Limits of Automatic Residential Electric Load Shaping”, Energies 12, (2019), doi: 10.3390/ en12173204
  • [19] M. Kott, “The electricity Consumption in Polish Households”, Modern Electr. Power Syst. 2015 – MEPS’15, Wrocław, Poland, July 6‒9, 2015, doi: 10.1109/MEPS.2015.7477166 8 P. Kapler Bull. Pol. Ac.: Tech. 69(1) 2021, e136216
  • [20] O. Elma and U.S. Selamogullar, “A Survey of a Residential Load Profile for Demand Side Managemenet Systems”, The 5th IEEE Internationl Conference on Smart Energy Grid Enegineering, 2017, doi: 10.1109/SEGE.2017.8052781
  • [21] P. Kapler, “Utilization of the adaptive potential of individual power consumers in interaction with power system”, Ph.D. Thesis, Warsaw University of Technology, Faculty of Electrical Engineering, (2018), [in Polish].
  • [22] A. Grinsted, J.C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to geophysical time series”, Nonlinear Process Geophys. European Geosciences Union (EGU), 11(5/6), 561‒566, (2004), doi: 10.5194/npg-11-561-2004
  • [23] B. Cazelles, M. Chavez, D. Berteaux, F. Menard, J.O. Vik, S. Jenouvrier, and N. C. Stenseth, “Wavelet analysis of ecological time series”, Oecologia 156, 287‒304 (2008), doi: 10.1007/ s00442-008-0993-2
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eb4bb423-f9c3-4a14-9fbf-3f7e0982f6fb
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