IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7421-d674293.html
   My bibliography  Save this article

Three-Phase State Estimation of a Low-Voltage Distribution Network with Kalman Filter

Author

Listed:
  • Fabio Napolitano

    (Department of Electrical, Electronic and Information, Engineering, University of Bologna, 40133 Bologna, Italy)

  • Juan Diego Rios Penaloza

    (Department of Electrical, Electronic and Information, Engineering, University of Bologna, 40133 Bologna, Italy)

  • Fabio Tossani

    (Department of Electrical, Electronic and Information, Engineering, University of Bologna, 40133 Bologna, Italy)

  • Alberto Borghetti

    (Department of Electrical, Electronic and Information, Engineering, University of Bologna, 40133 Bologna, Italy)

  • Carlo Alberto Nucci

    (Department of Electrical, Electronic and Information, Engineering, University of Bologna, 40133 Bologna, Italy)

Abstract

The state estimation of distribution networks has long been considered a challenging task for the reduced availability of real-time measures with respect to the transmission network case. This issue is expected to be improved by the deployment of modern smart meters that can be polled at relatively short time intervals. On the other hand, the management of the information coming from many heterogeneous meters still poses major issues. If low-voltage distribution systems are of interest, a three-phase formulation should be employed for the state estimation due to the typical load imbalance. Moreover, smart meter data may not be perfectly synchronized. This paper presents the implementation of a three-phase state estimation algorithm of a real portion of a low-voltage distribution network with distributed generation equipped with smart meters. The paper compares the typical state estimation algorithm that implements the weighted least squares method with an algorithm based on an iterated Kalman filter. The influence of nonsynchronicity of measurements and of delays in communication and processing is analyzed for both approaches.

Suggested Citation

  • Fabio Napolitano & Juan Diego Rios Penaloza & Fabio Tossani & Alberto Borghetti & Carlo Alberto Nucci, 2021. "Three-Phase State Estimation of a Low-Voltage Distribution Network with Kalman Filter," Energies, MDPI, vol. 14(21), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7421-:d:674293
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7421/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/21/7421/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmad, Fiaz & Rasool, Akhtar & Ozsoy, Emre & Sekar, Raja & Sabanovic, Asif & Elitaş, Meltem, 2018. "Distribution system state estimation-A step towards smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2659-2671.
    2. Karthikeyan Nainar & Florin Iov, 2020. "Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids," Energies, MDPI, vol. 13(20), pages 1-18, October.
    Full references (including those not matched with items on IDEAS)

    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. Karthikeyan Nainar & Florin Iov, 2021. "Three-Phase State Estimation for Distribution-Grid Analytics," Clean Technol., MDPI, vol. 3(2), pages 1-14, May.
    2. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    3. Edward J. Smith & Duane A. Robinson & Sean Elphick, 2024. "DER Control and Management Strategies for Distribution Networks: A Review of Current Practices and Future Directions," Energies, MDPI, vol. 17(11), pages 1-40, May.
    4. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    5. Karthikeyan Nainar & Florin Iov, 2020. "Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids," Energies, MDPI, vol. 13(20), pages 1-18, October.
    6. Jingyeong Park & Daisuke Kodaira & Kofi Afrifa Agyeman & Taeyoung Jyung & Sekyung Han, 2021. "Adaptive Power Flow Prediction Based on Machine Learning," Energies, MDPI, vol. 14(13), pages 1-18, June.
    7. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    8. Ivan Alymov & Moshe Averbukh, 2024. "Monitoring Energy Flows for Efficient Electricity Control in Low-Voltage Smart Grids," Energies, MDPI, vol. 17(9), pages 1-17, April.
    9. Israa T. Aziz & Hai Jin & Ihsan H. Abdulqadder & Sabah M. Alturfi & Wisam H. Alobaidi & Firas M.F. Flaih, 2019. "T 2 S 2 G: A Novel Two-Tier Secure Smart Grid Architecture to Protect Network Measurements," Energies, MDPI, vol. 12(13), pages 1-24, July.
    10. István Táczi & Bálint Sinkovics & István Vokony & Bálint Hartmann, 2021. "The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review," Energies, MDPI, vol. 14(17), pages 1-17, August.
    11. Aqdas Naz & Nadeem Javaid & Muhammad Babar Rasheed & Abdul Haseeb & Musaed Alhussein & Khursheed Aurangzeb, 2019. "Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid," Sustainability, MDPI, vol. 11(10), pages 1-22, May.
    12. Leila Kamyabi & Tek Tjing Lie & Samaneh Madanian & Sarah Marshall, 2024. "A Comprehensive Review of Hybrid State Estimation in Power Systems: Challenges, Opportunities and Prospects," Energies, MDPI, vol. 17(19), pages 1-20, September.
    13. Margossian, Harag & Kfouri, Ronald & Saliba, Rita, 2023. "Measurement protection to prevent cyber–physical attacks against power system State Estimation," International Journal of Critical Infrastructure Protection, Elsevier, vol. 43(C).
    14. Guoli Feng & Zhihao Ye & Yihui Xia & Heng Nian & Liming Huang & Zerun Wang, 2022. "High Frequency Resonance Suppression Strategy of Three-Phase Four-Wire Split Capacitor Inverter Connected to Parallel Compensation Grid," Energies, MDPI, vol. 15(4), pages 1-20, February.
    15. Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
    16. Ruipeng Guo & Lilan Dong & Hao Wu & Fangdi Hou & Chen Fang, 2021. "A Practical GERI-Based Method for Identifying Multiple Erroneous Parameters and Measurements Simultaneously," Energies, MDPI, vol. 14(12), pages 1-21, June.
    17. Guoli Feng & Zhihao Ye & Yihui Xia & Liming Huang & Zerun Wang, 2022. "Impedance Modeling and Stability Analysis of Three-Phase Four-Wire Inverter with Grid-Connected Operation," Energies, MDPI, vol. 15(8), pages 1-26, April.
    18. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    19. Zhang, Dongdong & Li, Chunjiao & Goh, Hui Hwang & Ahmad, Tanveer & Zhu, Hongyu & Liu, Hui & Wu, Thomas, 2022. "A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems," Renewable Energy, Elsevier, vol. 189(C), pages 1383-1406.
    20. Abouzar Estebsari & Luca Barbierato & Alireza Bahmanyar & Lorenzo Bottaccioli & Enrico Macii & Edoardo Patti, 2019. "A SGAM-Based Test Platform to Develop a Scheme for Wide Area Measurement-Free Monitoring of Smart Grids under High PV Penetration," Energies, MDPI, vol. 12(8), pages 1-27, April.

    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:21:p:7421-:d:674293. 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.