IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v79y2017icp839-849.html
   My bibliography  Save this article

Power system oscillations and control: Classifications and PSSs’ design methods: A review

Author

Listed:
  • Assi Obaid, Zeyad
  • Cipcigan, L.M.
  • Muhssin, Mazin T.

Abstract

In this paper, a review of the classifications of power system oscillation modes, as well as power system stabilizer (PSS) design structures, is proposed. Four major oscillation modes and their effects on power system stability have been investigated and analyzed, and the critical elements affecting each mode, frequency range, and PSS application have been summarized using important published work. Next, the PSS's structure has been classified according to the number of inputs and compensation filters, and a combination of the PSS with the intelligent systems, optimal evolutionary-based, and non-intelligent adaptive-based PSS has been highlighted. The effect of the oscillation modes in Great Britain's (GB) power system has been identified, as well as the possible solutions to damp this oscillation. It was found that the inter-area and the local machine modes have a greater impact on wide area power system stability and sustainability. Integrating new Renewable Energy Resources (RESs) can lead to more transient and dynamic instability. Therefore, more research is required to design solutions to tackle this grave problem. Four PSSs presented in the literature have been applied and tested in two different multi-machine Benchmark systems.

Suggested Citation

  • Assi Obaid, Zeyad & Cipcigan, L.M. & Muhssin, Mazin T., 2017. "Power system oscillations and control: Classifications and PSSs’ design methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 839-849.
  • Handle: RePEc:eee:rensus:v:79:y:2017:i:c:p:839-849
    DOI: 10.1016/j.rser.2017.05.103
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032117307499
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2017.05.103?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cuk Supriyadi, A.N. & Takano, H. & Murata, J. & Goda, T., 2014. "Adaptive robust PSS to enhance stabilization of interconnected power systems with high renewable energy penetration," Renewable Energy, Elsevier, vol. 63(C), pages 767-774.
    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. Zhao, Zhigao & Yang, Jiandong & Chung, C.Y. & Yang, Weijia & He, Xianghui & Chen, Man, 2021. "Performance enhancement of pumped storage units for system frequency support based on a novel small signal model," Energy, Elsevier, vol. 234(C).
    2. Zhao, Zhigao & Yang, Jiandong & Huang, Yifan & Yang, Weijia & Ma, Weichao & Hou, Liangyu & Chen, Man, 2021. "Improvement of regulation quality for hydro-dominated power system: quantifying oscillation characteristic and multi-objective optimization," Renewable Energy, Elsevier, vol. 168(C), pages 606-631.
    3. Jong Ju Kim & June Ho Park, 2021. "A Novel Structure of a Power System Stabilizer for Microgrids," Energies, MDPI, vol. 14(4), pages 1-33, February.
    4. Carlos Restrepo & Nicolas Yanẽz-Monsalvez & Catalina González-Castaño & Samir Kouro & Jose Rodriguez, 2021. "A Fast Converging Hybrid MPPT Algorithm Based on ABC and P&O Techniques for a Partially Shaded PV System," Mathematics, MDPI, vol. 9(18), pages 1-25, September.

    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. Zhang, Guozhou & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2021. "A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration," Renewable Energy, Elsevier, vol. 178(C), pages 363-376.

    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:eee:rensus:v:79:y:2017:i:c:p:839-849. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.