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The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System

Author

Listed:
  • Vojtech Blazek

    (Department of Electrical Power Engineering, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic
    CENTRE ENET at VŠB, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic)

  • Michal Petruzela

    (Department of Electrical Power Engineering, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic
    CENTRE ENET at VŠB, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic)

  • Tomas Vantuch

    (CENTRE ENET at VŠB, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic
    Department of Computer Science at Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic)

  • Zdenek Slanina

    (Department of Cybernetic and Biomedical Engineering, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic)

  • Stanislav Mišák

    (Department of Electrical Power Engineering, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic
    CENTRE ENET at VŠB, VSB Technical University of Ostrava, 17.listopadu 15, 70800 Ostrava, Czech Republic)

  • Wojciech Walendziuk

    (Faculty of Electrical Engineering, Bialystok University of Technology, Wiejska 45D, 15351 Bialystok, Poland)

Abstract

This article presents the analysis of the influence of household appliances on the quality of the energy consumed by the end-user. The results of the research, then, concern the final consumer (the lowest level of the power grid). The research was conducted on 120 combinations of electrical appliances connected into a grid. Each combination consisted of three devices working simultaneously in a micro-grid. The obtained and statistically analyzed data proved that there are several types of appliances that have a great influence on the power quality (PQ) parameters changes. The results of the conducted experiments indicate the devices which influenced significantly the total harmonic distortion of voltage (THDV), the voltage frequency (FREQ) and the voltage fluctuation (V). Specific features of particular devices were examined in terms of their significance for the power quality deviation. This showed the most important features which should be considered while working out the prediction model. The future of smart grids resides in data analysis, predictive models and real-time optimization. One of the key characteristics is the reducing energy consumption generated by renewable energy sources. This phenomenon, namely looking for problems connected with sustainable power quality and their appropriate solution, is described in this article. We performed an extended analysis of the smart home appliances influence of individual quantities on a real model. Furthermore, we explored devices with a high impact on chosen power quality indicators. In the end, we discuss their specific behavior and relevance to the above-described phenomenon to improve the predictive model utility.

Suggested Citation

  • Vojtech Blazek & Michal Petruzela & Tomas Vantuch & Zdenek Slanina & Stanislav Mišák & Wojciech Walendziuk, 2020. "The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System," Energies, MDPI, vol. 13(17), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4323-:d:401893
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    References listed on IDEAS

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    1. Mahela, Om Prakash & Shaik, Abdul Gafoor & Gupta, Neeraj, 2015. "A critical review of detection and classification of power quality events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 495-505.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Ghadimi, Noradin & Akbarimajd, Adel & Shayeghi, Hossein & Abedinia, Oveis, 2018. "Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting," Energy, Elsevier, vol. 161(C), pages 130-142.
    4. Daiva Stanelyte & Virginijus Radziukynas, 2019. "Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks," Energies, MDPI, vol. 13(1), pages 1-26, December.
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    Cited by:

    1. Ibrahim Salem Jahan & Vojtech Blazek & Stanislav Misak & Vaclav Snasel & Lukas Prokop, 2022. "Forecasting of Power Quality Parameters Based on Meteorological Data in Small-Scale Household Off-Grid Systems," Energies, MDPI, vol. 15(14), pages 1-20, July.

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