IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p7944-d689254.html
   My bibliography  Save this article

Adaptive Tracking Method of Distorted Voltage Using IMM Algorithm under Grid Frequency Fluctuation Conditions

Author

Listed:
  • Haoyao Nie

    (School of Economics and Management, Nanchang University, Nanchang 330031, China
    I.H. Asper School of Business, University of Manitoba, Winnipeg, MB R3T 5V4, Canada)

  • Xiaohua Nie

    (Department of Energy and Electrical Engineering, Nanchang University, Nanchang 330031, China)

Abstract

This paper newly proposes an interactive multiple model (IMM) algorithm to adaptively track distorted AC voltage with the grid frequency fluctuation. The usual tracking methods are Kalman filter (KF) algorithm with a fixed frequency and KF algorithm with frequency identifier. The KF algorithm with a fixed frequency has a larger covariance parameter to guarantee the tracking robustness. However, it has a large tracking error. The KF algorithm with frequency identifier overly depends on the accuracy and stability of frequency identifier. The advantage of the proposed method is that it is decoupled from frequency detection and does not depend on frequency detection accuracy. First, the orthogonal vector dynamic (OVD) tracking model of the sine wave is established. Then, a model set covering the grid frequency fluctuation range is formed, and a new OVD-IMM tracking algorithm is proposed in combination with IMM algorithm. In the end, the effectiveness and accuracy of the proposed OVD-IMM algorithm are verified through simulations and experiments.

Suggested Citation

  • Haoyao Nie & Xiaohua Nie, 2021. "Adaptive Tracking Method of Distorted Voltage Using IMM Algorithm under Grid Frequency Fluctuation Conditions," Energies, MDPI, vol. 14(23), pages 1-17, November.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7944-:d:689254
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/7944/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/23/7944/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Benjamin Schäfer & Christian Beck & Kazuyuki Aihara & Dirk Witthaut & Marc Timme, 2018. "Non-Gaussian power grid frequency fluctuations characterized by Lévy-stable laws and superstatistics," Nature Energy, Nature, vol. 3(2), pages 119-126, February.
    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. Zhou, Hao & Toth, Christopher & Guensler, Randall & Laval, Jorge, 2022. "Hybrid modeling of lane changes near freeway diverges," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 1-14.
    2. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    3. Chen, Xiaodong & Ge, Xinxin & Sun, Rongfu & Wang, Fei & Mi, Zengqiang, 2024. "A SVM based demand response capacity prediction model considering internal factors under composite program," Energy, Elsevier, vol. 300(C).
    4. Konlechner, Roland & Allagui, Anis & Antonov, Vladimir N. & Yudin, Dmitry, 2023. "A superstatistics approach to the modelling of memristor current–voltage responses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    5. Santiago Bustamante-Mesa & Jorge W. Gonzalez-Sanchez & Sergio D. Saldarriaga-Zuluaga & Jesús M. López-Lezama & Nicolás Muñoz-Galeano, 2024. "Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection," Energies, MDPI, vol. 17(2), pages 1-20, January.
    6. Davis, Sergio, 2022. "Fluctuating temperature outside superstatistics: Thermodynamics of small systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    7. dos Santos, M.A.F. & Menon, L. & Cius, D., 2022. "Superstatistical approach of the anomalous exponent for scaled Brownian motion," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    8. Davis, Sergio, 2022. "A classification of nonequilibrium steady states based on temperature correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    9. Todeschini, G. & Coles, D. & Lewis, M. & Popov, I. & Angeloudis, A. & Fairley, I. & Johnson, F. & Williams, A.J. & Robins, P. & Masters, I., 2022. "Medium-term variability of the UK's combined tidal energy resource for a net-zero carbon grid," Energy, Elsevier, vol. 238(PA).

    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:23:p:7944-:d:689254. 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.