IDEAS home Printed from https://ideas.repec.org/a/gam/jcltec/v3y2021i2p22-408d547491.html
   My bibliography  Save this article

Three-Phase State Estimation for Distribution-Grid Analytics

Author

Listed:
  • Karthikeyan Nainar

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Florin Iov

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

Abstract

Power-distribution grids consist of assets such as transformers, cables, and switches, of which the proper utilization is essential for the provision of a secure and reliable power supply to end customers. Distribution-system operators (DSOs) are responsible for the operation and maintenance of these assets. Due to the increased use of renewable sources such as wind and solar, grid assets are prone to operation conditions outside safe boundaries, such as overloading, large voltage unbalance, and a rise in voltage. At present, distribution grids are poorly monitored by DSOs, and the above-mentioned problems may thereby go unnoticed until the failure of a critical asset occurs. The deployment of smart meters in distribution grids has enabled measurements of grid variables such as power, current, and voltage. However, their measurements are used only for billing purposes, and not for monitoring and improving the operating condition of distribution grids. In this paper, a state-estimation algorithm is proposed that utilizes smart-meter data for offline analysis, and estimates the loading of grid assets and power losses. Single- and three-phase state-estimation algorithms are compared through simulation studies on a real-life low-voltage distribution grid using measured smart-meter data. The three-phase state-estimation algorithm based on the nonlinear weighted least-squares method was found to be more accurate in estimating cable loading and line power losses. The proposed method is useful for DSOs to analyze power flows in their distribution grids and take necessary actions such as grid upgrades or the rerouting of power flows.

Suggested Citation

  • Karthikeyan Nainar & Florin Iov, 2021. "Three-Phase State Estimation for Distribution-Grid Analytics," Clean Technol., MDPI, vol. 3(2), pages 1-14, May.
    • Handle: RePEc:gam:jcltec:v:3:y:2021:i:2:p:22-408:d:547491
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-8797/3/2/22/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-8797/3/2/22/
    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. Thiago Mota Soares & Ubiratan Holanda Bezerra & Maria Emília de Lima Tostes, 2019. "Full-Observable Three-Phase State Estimation Algorithm Applied to Electric Distribution Grids," Energies, MDPI, vol. 12(7), pages 1-16, April.
    3. 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, 2020. "Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids," Energies, MDPI, vol. 13(20), pages 1-18, October.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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. Giovanni Artale & Giuseppe Caravello & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Salvatore Guaiana & Ninh Nguyen Quang & Marco Palmeri & Nicola Panzavecchia & Giovanni Tinè, 2020. "A Virtual Tool for Load Flow Analysis in a Micro-Grid," Energies, MDPI, vol. 13(12), pages 1-26, June.
    16. 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.
    17. Giovanni Artale & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Salvatore Guaiana & Enrico Telaretti & Nicola Panzavecchia & Giovanni Tinè, 2019. "Incremental Heuristic Approach for Meter Placement in Radial Distribution Systems," Energies, MDPI, vol. 12(20), pages 1-17, October.
    18. David Macii & Daniele Fontanelli & Grazia Barchi, 2020. "A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses," Energies, MDPI, vol. 13(22), pages 1-25, November.
    19. 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.
    20. 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.

    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:jcltec:v:3:y:2021:i:2:p:22-408:d:547491. 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.