IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i17p4323-d401893.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/17/4323/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/17/4323/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thiemo Fetzer & Samuel Marden, 2017. "Take What You Can: Property Rights, Contestability and Conflict," Economic Journal, Royal Economic Society, vol. 0(601), pages 757-783, May.
    2. Daniel Agness & Travis Baseler & Sylvain Chassang & Pascaline Dupas & Erik Snowberg, 2022. "Valuing the Time of the Self-Employed," Working Papers 2022-2, Princeton University. Economics Department..
    3. Orietta Nicolis & Jean Paul Maidana & Fabian Contreras & Danilo Leal, 2024. "Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    4. Sun, Yougang & Xu, Junqi & Lin, Guobin & Ni, Fei & Simoes, Rolando, 2018. "An optimal performance based new multi-objective model for heat and power hub in large scale users," Energy, Elsevier, vol. 161(C), pages 1234-1249.
    5. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    6. Nantian Huang & Hua Peng & Guowei Cai & Jikai Chen, 2016. "Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm," Energies, MDPI, vol. 9(11), pages 1-21, November.
    7. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    8. Forzani, Liliana & Gieco, Antonella & Tolmasky, Carlos, 2017. "Likelihood ratio test for partial sphericity in high and ultra-high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 18-38.
    9. Yujia Li & Xiangrui Zeng & Chien‐Wei Lin & George C. Tseng, 2022. "Simultaneous estimation of cluster number and feature sparsity in high‐dimensional cluster analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 574-585, June.
    10. Azcarate, I. & Gutierrez, J.J. & Lazkano, A. & Saiz, P. & Redondo, K. & Leturiondo, L.A., 2016. "Towards limiting the sensitivity of energy-efficient lighting to voltage fluctuations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1384-1395.
    11. Caruso, Germán & Scartascini, Carlos & Tommasi, Mariano, 2015. "Are we all playing the same game? The economic effects of constitutions depend on the degree of institutionalization," European Journal of Political Economy, Elsevier, vol. 38(C), pages 212-228.
    12. Mehmet Çağlar & Cem Gürler, 2022. "Sustainable Development Goals: A cluster analysis of worldwide countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8593-8624, June.
    13. Jelle R Dalenberg & Luca Nanetti & Remco J Renken & René A de Wijk & Gert J ter Horst, 2014. "Dealing with Consumer Differences in Liking during Repeated Exposure to Food; Typical Dynamics in Rating Behavior," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    14. Daniel Lewis & Davide Melcangi & Laura Pilossoph, 2019. "Latent Heterogeneity in the Marginal Propensity to Consume," 2019 Meeting Papers 519, Society for Economic Dynamics.
    15. Misael Lopez-Ramirez & Eduardo Cabal-Yepez & Luis M. Ledesma-Carrillo & Homero Miranda-Vidales & Carlos Rodriguez-Donate & Rocio A. Lizarraga-Morales, 2018. "FPGA-Based Online PQD Detection and Classification through DWT, Mathematical Morphology and SVD," Energies, MDPI, vol. 11(4), pages 1-15, March.
    16. Chun-Xia Zhang & Jiang-She Zhang & Sang-Woon Kim, 2016. "PBoostGA: pseudo-boosting genetic algorithm for variable ranking and selection," Computational Statistics, Springer, vol. 31(4), pages 1237-1262, December.
    17. Tümay, Mehmet & Demirdelen, Tuğçe & Bal, Selva & Kayaalp, Rahmi İlker & Doğru, Burcu & Aksoy, Mahmut, 2017. "A review of magnetically controlled shunt reactor for power quality improvement with renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 215-228.
    18. Lin, Zi & Liu, Xiaolei & Lao, Liyun & Liu, Hengxu, 2020. "Prediction of two-phase flow patterns in upward inclined pipes via deep learning," Energy, Elsevier, vol. 210(C).
    19. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    20. Germán Caruso & Walter Sosa-Escudero & Marcela Svarc, 2015. "Deprivation and the Dimensionality of Welfare: A Variable-Selection Cluster-Analysis Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(4), pages 702-722, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4323-:d:401893. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.