IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i9p3708-d1133286.html
   My bibliography  Save this article

Review of RoCoF Estimation Techniques for Low-Inertia Power Systems

Author

Listed:
  • Xiaoyu Deng

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China)

  • Ruo Mo

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China)

  • Pengliang Wang

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China)

  • Junru Chen

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China)

  • Dongliang Nan

    (Electric Power Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830011, China)

  • Muyang Liu

    (School of Electrical Engineering, Xinjiang University, Urumqi 830046, China)

Abstract

As the traditional generation is gradually replaced by inverter-based resources, a lack of rotational inertia is now a common issue of modern power systems, which leads to an increasingly larger rate of change of frequency (RoCoF) following contingencies and may result in frequency collapse. As a crucial index of the frequency security and stability of power systems, the accurate estimation of the RoCoF can be a foundation for the development of advanced operations and control techniques of the future power system. This paper firstly analyzes the role of the RoCoF in typical blackouts occurring in recent years and discusses the physical and numerical nature of the RoCoF; then, by introducing the frequency spatial distribution of the power system, the paper discusses the concept of the “center” RoCoF that can present the frequency security and stability of the entire system. The estimation and prediction techniques of the maximal power system RoCoF following a contingency and the existing real-time tracking techniques of the power system RoCoF are comprehensively reviewed. Finally, the open questions and related research topics of the RoCoF estimation are discussed.

Suggested Citation

  • Xiaoyu Deng & Ruo Mo & Pengliang Wang & Junru Chen & Dongliang Nan & Muyang Liu, 2023. "Review of RoCoF Estimation Techniques for Low-Inertia Power Systems," Energies, MDPI, vol. 16(9), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3708-:d:1133286
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/9/3708/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/9/3708/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krzysztof Szabat & Karol Wróbel & Krzysztof Dróżdż & Dariusz Janiszewski & Tomasz Pajchrowski & Adrian Wójcik, 2020. "A Fuzzy Unscented Kalman Filter in the Adaptive Control System of a Drive System with a Flexible Joint," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Jiaxin Wen & Siqi Bu & Bowen Zhou & Qiyu Chen & Dongsheng Yang, 2020. "A Fast-Algorithmic Probabilistic Evaluation on Regional Rate of Change of Frequency (RoCoF) for Operational Planning of High Renewable Penetrated Power Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.
    3. Wen, Jiaxin & Bu, Siqi & Li, Fangxing & Du, Pengwei, 2021. "Risk assessment and mitigation on area-level RoCoF for operational planning," Energy, Elsevier, vol. 228(C).
    4. Zengqin Li & Weifeng Zhang & Zhiyuan Zhuang & Tao Jin, 2022. "A Novel Synchrophasor Estimation Based on Enhanced All-Phase DFT with Iterative Compensation and Its Implementation," Energies, MDPI, vol. 15(19), pages 1-17, September.
    5. Xuehua Wu & Qianqian Qian & Yuqing Bao, 2022. "Demand Response Using Disturbance Estimation-Based Kalman Filtering for the Frequency Control," Energies, MDPI, vol. 15(24), pages 1-14, 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. Matheus Schramm Dall’Asta & Telles Brunelli Lazzarin, 2024. "A Review of Fast Power-Reserve Control Techniques in Grid-Connected Wind Energy Conversion Systems," Energies, MDPI, vol. 17(2), pages 1-29, January.
    2. Alisher Askarov & Vladimir Rudnik & Nikolay Ruban & Pavel Radko & Pavel Ilyushin & Aleksey Suvorov, 2024. "Enhanced Virtual Synchronous Generator with Angular Frequency Deviation Feedforward and Energy Recovery Control for Energy Storage System," Mathematics, MDPI, vol. 12(17), pages 1-26, August.

    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. Karol Wróbel & Kacper Śleszycki & Krzysztof Szabat & Seiichiro Katsura, 2021. "Application of Multilayer Observer for a Drive System with Flexibility," Energies, MDPI, vol. 14(24), pages 1-19, December.
    2. Konrad Urbanski & Dariusz Janiszewski, 2021. "Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks," Energies, MDPI, vol. 14(23), pages 1-17, December.
    3. Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.
    4. Karol Wróbel & Kacper Śleszycki & Amanuel Haftu Kahsay & Krzysztof Szabat & Seiichiro Katsura, 2023. "Robust Speed Control of Uncertain Two-Mass System," Energies, MDPI, vol. 16(17), pages 1-17, August.
    5. Dominik Łuczak, 2021. "Nonlinear Identification with Constraints in Frequency Domain of Electric Direct Drive with Multi-Resonant Mechanical Part," Energies, MDPI, vol. 14(21), pages 1-12, November.
    6. Feng Guo & David Schlipf, 2021. "A Spectral Model of Grid Frequency for Assessing the Impact of Inertia Response on Wind Turbine Dynamics," Energies, MDPI, vol. 14(9), pages 1-19, April.
    7. Radosław Nalepa & Karol Najdek & Karol Wróbel & Krzysztof Szabat, 2020. "Application of D-Decomposition Technique to Selection of Controller Parameters for a Two-Mass Drive System," Energies, MDPI, vol. 13(24), pages 1-21, December.
    8. Marcin Kamiński & Krzysztof Szabat, 2021. "Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft," Energies, MDPI, vol. 14(12), pages 1-26, June.
    9. Lasantha Meegahapola & Siqi Bu, 2021. "Special Issue: “Wind Power Integration into Power Systems: Stability and Control Aspects”," Energies, MDPI, vol. 14(12), pages 1-4, June.

    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:16:y:2023:i:9:p:3708-:d:1133286. 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.