IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v103y2016icp215-230.html
   My bibliography  Save this article

Inequality constrained nonlinear data reconciliation of a steam turbine power plant for enhanced parameter estimation

Author

Listed:
  • Guo, Sisi
  • Liu, Pei
  • Li, Zheng

Abstract

Measurement errors inevitably exist amongst on-line measured data in steam turbine power plants. Problematic steam turbine isentropic efficiencies and turbine expansion curves are therefore obtained in the existence of measurement errors. Data reconciliation is widely used for uncertainty reduction of measurements and parameter estimation. Inequality constraints or bounds are rather necessary in some cases to adjust parameter estimates to be physically meaningful. In this work, we apply an inequality constrained nonlinear data reconciliation approach to the thermal system of a power plant, and compare its effect with equality constrained approaches. Case studies using performance test data and operational measurement data of a real-life 1000 MW steam turbine power plant are provided. The necessity and difference brought by inequality constraints are also discussed. Corrected expansion curve with reasonable enthalpy–entropy relationships and better estimates of isentropic efficiencies are obtained after implementation of inequality constraints. Results show that uncertainties of most measured parameters are reduced by 30–80 percent, and uncertainty of the calculated exhaust steam enthalpy is reduced by 22 percent.

Suggested Citation

  • Guo, Sisi & Liu, Pei & Li, Zheng, 2016. "Inequality constrained nonlinear data reconciliation of a steam turbine power plant for enhanced parameter estimation," Energy, Elsevier, vol. 103(C), pages 215-230.
    • Handle: RePEc:eee:energy:v:103:y:2016:i:c:p:215-230
    DOI: 10.1016/j.energy.2016.02.158
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216302201
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.02.158?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Jianxi & Han, Wenquan & Chen, Kang & Liu, Pei & Li, Zheng, 2022. "Gross error detection in steam turbine measurements based on data reconciliation of inequality constraints," Energy, Elsevier, vol. 253(C).
    2. Szega, Marcin, 2018. "Issues of an optimization of measurements location in redundant measurements systems of an energy conversion process – A case study," Energy, Elsevier, vol. 165(PA), pages 1034-1047.
    3. Guo, Sisi & Liu, Pei & Li, Zheng, 2018. "Enhancement of performance monitoring of a coal-fired power plant via dynamic data reconciliation," Energy, Elsevier, vol. 151(C), pages 203-210.
    4. Chen, Heng & Wu, Yunyun & Qi, Zhen & Chen, Qiao & Xu, Gang & Yang, Yongping & Liu, Wenyi, 2019. "Improved combustion air preheating design using multiple heat sources incorporating bypass flue in large-scale coal-fired power unit," Energy, Elsevier, vol. 169(C), pages 527-541.
    5. Guo, Sisi & Liu, Pei & Li, Zheng, 2016. "Identification and isolability of multiple gross errors in measured data for power plants," Energy, Elsevier, vol. 114(C), pages 177-187.
    6. Yu, Jianxi & Liu, Pei & Li, Zheng, 2021. "Data reconciliation of the thermal system of a double reheat power plant for thermal calculation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    7. Eslick, John C. & Zamarripa, Miguel A. & Ma, Jinliang & Wang, Maojian & Bhattacharya, Indrajit & Rychener, Brian & Pinkston, Philip & Bhattacharyya, Debangsu & Zitney, Stephen E. & Burgard, Anthony P., 2022. "Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application," Applied Energy, Elsevier, vol. 319(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:energy:v:103:y:2016:i:c:p:215-230. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.journals.elsevier.com/energy .

    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.