IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i4p1173-d503844.html
   My bibliography  Save this article

Application of Decision Trees for Optimal Allocation of Harmonic Filters in Medium-Voltage Networks

Author

Listed:
  • Maciej Klimas

    (Electrical Engineering and Computer Science Department, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Dariusz Grabowski

    (Electrical Engineering and Computer Science Department, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Dawid Buła

    (Electrical Engineering and Computer Science Department, Faculty of Electrical Engineering, Silesian University of Technology, 44-100 Gliwice, Poland)

Abstract

The paper proposes a solution for the problem of optimizing medium voltage power systems which supply, among others, nonlinear loads. It is focused on decision tree (DT) application for the sizing and allocation of active power filters (APFs), which are the most effective means of power quality improvement. Propositions of some DT strategies followed by the results have been described in the paper. On the basis of an example of a medium-voltage network, an analysis of the selection of the number and allocation of active power filters was carried out in terms of minimizing losses and costs keeping under control voltage total harmonic distortion (THD) coefficients in the network nodes. The presented example shows that decision trees allow for the selection of the optimal solution, depending on assumed limitations, expected effects, and costs.

Suggested Citation

  • Maciej Klimas & Dariusz Grabowski & Dawid Buła, 2021. "Application of Decision Trees for Optimal Allocation of Harmonic Filters in Medium-Voltage Networks," Energies, MDPI, vol. 14(4), pages 1-24, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1173-:d:503844
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/4/1173/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/4/1173/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiangfei Meng & Pei Zhang & Dahai Zhang, 2020. "Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms," Energies, MDPI, vol. 13(15), pages 1-13, July.
    2. Dawid Buła & Dariusz Grabowski & Michał Lewandowski & Marcin Maciążek & Anna Piwowar, 2020. "Software Solution for Modeling, Sizing, and Allocation of Active Power Filters in Distribution Networks," Energies, MDPI, vol. 14(1), pages 1-25, December.
    3. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    4. Buła, D. & Lewandowski, M., 2015. "Comparison of frequency domain and time domain model of a distributed power supplying system with active power filters (APFs)," Applied Mathematics and Computation, Elsevier, vol. 267(C), pages 771-779.
    5. Buła, D. & Lewandowski, M., 2018. "Steady state simulation of a distributed power supplying system using a simple hybrid time-frequency model," Applied Mathematics and Computation, Elsevier, vol. 319(C), pages 195-202.
    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. Hamed Rezapour & MohamadAli Amini & Hamid Falaghi & António M. Lopes, 2023. "Integration of Stand-Alone Controlled Active Power Filters in Harmonic Power Flow of Radial Distribution Networks," Energies, MDPI, vol. 16(5), pages 1-20, March.
    2. Gabriel Nicolae Popa, 2022. "Electric Power Quality through Analysis and Experiment," Energies, MDPI, vol. 15(21), pages 1-14, October.
    3. Dawid Buła & Dariusz Grabowski & Marcin Maciążek, 2022. "A Review on Optimization of Active Power Filter Placement and Sizing Methods," Energies, MDPI, vol. 15(3), pages 1-35, February.

    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. Dawid Buła & Dariusz Grabowski & Michał Lewandowski & Marcin Maciążek & Anna Piwowar, 2020. "Software Solution for Modeling, Sizing, and Allocation of Active Power Filters in Distribution Networks," Energies, MDPI, vol. 14(1), pages 1-25, December.
    2. Alvaro Furlani Bastos & Surya Santoso, 2021. "Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications," Energies, MDPI, vol. 14(2), pages 1-21, January.
    3. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    4. Alessandro Bosisio & Matteo Moncecchi & Andrea Morotti & Marco Merlo, 2021. "Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience," Energies, MDPI, vol. 14(14), pages 1-23, July.
    5. Robert Basmadjian & Amirhossein Shaafieyoun, 2023. "Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future," Energies, MDPI, vol. 16(16), pages 1-19, August.
    6. Merel Noorman & Brenda Espinosa Apráez & Saskia Lavrijssen, 2023. "AI and Energy Justice," Energies, MDPI, vol. 16(5), pages 1-16, February.
    7. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    8. Tayeb Brahimi, 2019. "Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia," Energies, MDPI, vol. 12(24), pages 1-16, December.
    9. Wolfram Rozas & Rafael Pastor-Vargas & Angel Miguel García-Vico & José Carpio, 2023. "Consumption–Production Profile Categorization in Energy Communities," Energies, MDPI, vol. 16(19), pages 1-27, October.
    10. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    11. Shahaboddin Shamshirband & Masoud Hadipoor & Alireza Baghban & Amir Mosavi & Jozsef Bukor & Annamária R. Várkonyi-Kóczy, 2019. "Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases," Mathematics, MDPI, vol. 7(10), pages 1-16, October.
    12. Duberney Murillo-Yarce & José Alarcón-Alarcón & Marco Rivera & Carlos Restrepo & Javier Muñoz & Carlos Baier & Patrick Wheeler, 2020. "A Review of Control Techniques in Photovoltaic Systems," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    13. Tseng, Fang-Mei & Palma Gil, Eunice Ina N. & Lu, Louis Y.Y., 2021. "Developmental trajectories of blockchain research and its major subfields," Technology in Society, Elsevier, vol. 66(C).
    14. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    15. Julián Ascencio-Vásquez & Jakob Bevc & Kristjan Reba & Kristijan Brecl & Marko Jankovec & Marko Topič, 2020. "Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy," Energies, MDPI, vol. 13(9), pages 1-12, May.
    16. Lily Popova Zhuhadar & Miltiadis D. Lytras, 2023. "The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    17. Wenninger, Simon & Kaymakci, Can & Wiethe, Christian, 2022. "Explainable long-term building energy consumption prediction using QLattice," Applied Energy, Elsevier, vol. 308(C).
    18. Saeed Nosratabadi & Amir Mosavi & Ramin Keivani & Sina Ardabili & Farshid Aram, 2020. "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability," Papers 2010.02670, arXiv.org.
    19. Kei Hirose & Keigo Wada & Maiya Hori & Rin-ichiro Taniguchi, 2020. "Event Effects Estimation on Electricity Demand Forecasting," Energies, MDPI, vol. 13(21), pages 1-20, November.
    20. Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.

    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:14:y:2021:i:4:p:1173-:d:503844. 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.