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

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  • 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
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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.

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