IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v343y2023ics0306261923005779.html
   My bibliography  Save this article

A low-quality PMU data identification method with dynamic criteria based on spatial–temporal correlations and random matrices

Author

Listed:
  • Song, Wenchao
  • Lu, Chao
  • Lin, Junjie
  • Fang, Chen
  • Liu, Shu

Abstract

With the high access of renewable energy, complex and changeable transmission networks, and frequent load interactions, the dynamic characteristics of low-carbon power systems have become more complex and random. The 10 ms-level dynamic measurement data of Phasor Measurement Unit are the basis for dynamic awareness, control and decision. However, the phenomenon of low-quality data usually exists in PMU measurements. Considering the spatial–temporal correlation reflected by the random matrix single-ring theorem and correlation coefficients, two system operating states and two types of low-quality PMU data are determined. Based on singular value decomposition and reconstruction, the distribution of the residuals between the original data and reconstructed data is analyzed to realize low-quality PMU data identification. To improve the identification accuracy, a dynamic threshold selection method of spatial–temporal correlation analysis is proposed for identification criteria. The feasibility and applicability of this method has been verified in the simulation data of IEEE 39 bus system and PMU measured data of the practical power grid.

Suggested Citation

  • Song, Wenchao & Lu, Chao & Lin, Junjie & Fang, Chen & Liu, Shu, 2023. "A low-quality PMU data identification method with dynamic criteria based on spatial–temporal correlations and random matrices," Applied Energy, Elsevier, vol. 343(C).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s0306261923005779
    DOI: 10.1016/j.apenergy.2023.121213
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923005779
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121213?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. Yang, Hongming & Liang, Rui & Yuan, Yuan & Chen, Bowen & Xiang, Sheng & Liu, Junpeng & Zhao, Huan & Ackom, Emmanuel, 2022. "Distributionally robust optimal dispatch in the power system with high penetration of wind power based on net load fluctuation data," Applied Energy, Elsevier, vol. 313(C).
    2. Brouwer, Anne Sjoerd & van den Broek, Machteld & Seebregts, Ad & Faaij, André, 2015. "Operational flexibility and economics of power plants in future low-carbon power systems," Applied Energy, Elsevier, vol. 156(C), pages 107-128.
    3. Qiu, Dawei & Wang, Yi & Sun, Mingyang & Strbac, Goran, 2022. "Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 313(C).
    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. Zhao, Xiaoli & Chen, Haoran & Liu, Suwei & Ye, Xiaomei, 2020. "Economic & environmental effects of priority dispatch of renewable energy considering fluctuating power output of coal-fired units," Renewable Energy, Elsevier, vol. 157(C), pages 695-707.
    2. Alimou, Yacine & Maïzi, Nadia & Bourmaud, Jean-Yves & Li, Marion, 2020. "Assessing the security of electricity supply through multi-scale modeling: The TIMES-ANTARES linking approach," Applied Energy, Elsevier, vol. 279(C).
    3. Igor Donskoy, 2023. "Techno-Economic Efficiency Estimation of Promising Integrated Oxyfuel Gasification Combined-Cycle Power Plants with Carbon Capture," Clean Technol., MDPI, vol. 5(1), pages 1-18, February.
    4. Rao, A. Gangoli & van den Oudenalder, F.S.C. & Klein, S.A., 2019. "Natural gas displacement by wind curtailment utilization in combined-cycle power plants," Energy, Elsevier, vol. 168(C), pages 477-491.
    5. Yang, Weijia & Yang, Jiandong, 2019. "Advantage of variable-speed pumped storage plants for mitigating wind power variations: Integrated modelling and performance assessment," Applied Energy, Elsevier, vol. 237(C), pages 720-732.
    6. Andrychowicz, Mateusz & Olek, Blazej & Przybylski, Jakub, 2017. "Review of the methods for evaluation of renewable energy sources penetration and ramping used in the Scenario Outlook and Adequacy Forecast 2015. Case study for Poland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 703-714.
    7. Chang, Miguel & Lund, Henrik & Thellufsen, Jakob Zinck & Østergaard, Poul Alberg, 2023. "Perspectives on purpose-driven coupling of energy system models," Energy, Elsevier, vol. 265(C).
    8. Adams, T. & Mac Dowell, N., 2016. "Off-design point modelling of a 420MW CCGT power plant integrated with an amine-based post-combustion CO2 capture and compression process," Applied Energy, Elsevier, vol. 178(C), pages 681-702.
    9. Liu, Fan & Duan, Jiandong & Wu, Chen & Tian, Qinxing, 2024. "Risk-averse distributed optimization for integrated electricity-gas systems considering uncertainties of Wind-PV and power-to-gas," Renewable Energy, Elsevier, vol. 227(C).
    10. Yin, Guangzhi & Duan, Maosheng, 2022. "Pricing the deep peak regulation service of coal-fired power plants to promote renewable energy integration," Applied Energy, Elsevier, vol. 321(C).
    11. Dongwen Chen & Zheng Chu, 2024. "Enhancing Power Supply Flexibility in Renewable Energy Systems with Optimized Energy Dispatch in Coupled CHP, Heat Pump, and Thermal Storage," Energies, MDPI, vol. 17(12), pages 1-29, June.
    12. Sachajdak, Andrzej & Lappalainen, Jari & Mikkonen, Hannu, 2019. "Dynamic simulation in development of contemporary energy systems – oxy combustion case study," Energy, Elsevier, vol. 181(C), pages 964-973.
    13. Lemmer, Andreas & Krümpel, Johannes, 2017. "Demand-driven biogas production in anaerobic filters," Applied Energy, Elsevier, vol. 185(P1), pages 885-894.
    14. Ping, Zuowei & Li, Xiuting & He, Wei & Yang, Tao & Yuan, Ye, 2020. "Sparse learning of network-reduced models for locating low frequency oscillations in power systems," Applied Energy, Elsevier, vol. 262(C).
    15. Jia, Ke & Li, Yanbin & Fang, Yu & Zheng, Liming & Bi, Tianshu & Yang, Qixun, 2018. "Transient current similarity based protection for wind farm transmission lines," Applied Energy, Elsevier, vol. 225(C), pages 42-51.
    16. Liu, Dong & Wang, Xin & Peng, Yunshui & Zhang, Hui & Xiao, Zhihuai & Han, Xiangdong & Malik, O.P., 2020. "Stability analysis of hydropower units under full operating conditions considering turbine nonlinearity," Renewable Energy, Elsevier, vol. 154(C), pages 723-742.
    17. van Zuijlen, Bas & Zappa, William & Turkenburg, Wim & van der Schrier, Gerard & van den Broek, Machteld, 2019. "Cost-optimal reliable power generation in a deep decarbonisation future," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    18. Qadrdan, Meysam & Ameli, Hossein & Strbac, Goran & Jenkins, Nicholas, 2017. "Efficacy of options to address balancing challenges: Integrated gas and electricity perspectives," Applied Energy, Elsevier, vol. 190(C), pages 181-190.
    19. Wang, Mingshen & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Yu, Xiaodan & Qi, Yan, 2017. "Active power regulation for large-scale wind farms through an efficient power plant model of electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1673-1683.
    20. Backe, Stian & Ahang, Mohammadreza & Tomasgard, Asgeir, 2021. "Stable stochastic capacity expansion with variable renewables: Comparing moment matching and stratified scenario generation sampling," Applied Energy, Elsevier, vol. 302(C).

    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:appene:v:343:y:2023:i:c:s0306261923005779. 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/405891/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.