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Data reconciliation and gross error detection for operational data in power plants

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  • Jiang, Xiaolong
  • Liu, Pei
  • Li, Zheng

Abstract

The quality of on-line measured operational data is usually not satisfactory for the performance monitoring of coal-fired power plants, due to the low accuracy of measuring instrument. Data reconciliation is a data preprocessing technique which can improve the accuracy of measured data through process modeling and optimization, and can also be used for gross error detection together with a statistical test method. In this work, we provide a mathematical framework for gross error detection in power plants via data reconciliation. We also provide case studies to implement the proposed framework in the feed water regenerative heating system of a real-life 1000 MW ultra-supercritical coal-fired power generation unit. Data reconciliation simulation results show that the relative root mean squared errors of the primary flow measurements, namely the outlet flow rate of the #1 feed water heater, the outlet flow rate of the feed water pump, and the inlet flow rate of condensate water in the deaerator are reduced by 72%, 40%, 22%. Simulation results also show that data reconciliation is effective for accuracy improvement when estimated error standard deviations are different from the actual ones and when random errors follow generalized normal distributions. We then provide a case study where gross error detection is performed together with a global test and a serial elimination strategy, and a gross error in the measurement of outlet flow rate of the feed water pump is successfully detected and validated by the on-site inspection and maintenance records of the power plant.

Suggested Citation

  • Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Data reconciliation and gross error detection for operational data in power plants," Energy, Elsevier, vol. 75(C), pages 14-23.
    • Handle: RePEc:eee:energy:v:75:y:2014:i:c:p:14-23
    DOI: 10.1016/j.energy.2014.03.024
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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.
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    3. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "A data reconciliation based framework for integrated sensor and equipment performance monitoring in power plants," Applied Energy, Elsevier, vol. 134(C), pages 270-282.
    4. Plis, Marcin & Rusinowski, Henryk, 2019. "Identification of mathematical models of thermal processes with reconciled measurement results," Energy, Elsevier, vol. 177(C), pages 192-202.
    5. Loyola-Fuentes, José & Smith, Robin, 2019. "Data reconciliation and gross error detection in crude oil pre-heat trains undergoing shell-side and tube-side fouling deposition," Energy, Elsevier, vol. 183(C), pages 368-384.
    6. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    7. Guo, Sisi & Liu, Pei & Li, Zheng, 2016. "Data reconciliation for the overall thermal system of a steam turbine power plant," Applied Energy, Elsevier, vol. 165(C), pages 1037-1051.
    8. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Gross error isolability for operational data in power plants," Energy, Elsevier, vol. 74(C), pages 918-927.
    9. 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).
    10. 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).
    11. Du, Zhimin & Chen, Ling & Jin, Xinqiao, 2017. "Data-driven based reliability evaluation for measurements of sensors in a vapor compression system," Energy, Elsevier, vol. 122(C), pages 237-248.
    12. 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.
    13. Wang, Yanhong & Cao, Lihua & Li, Xingcan & Wang, Jiaxing & Hu, Pengfei & Li, Bo & Li, Yong, 2020. "A novel thermodynamic method and insight of heat transfer characteristics on economizer for supercritical thermal power plant," Energy, Elsevier, vol. 191(C).
    14. Šomplák, Radovan & Nevrlý, Vlastimír & Smejkalová, Veronika & Šmídová, Zlata & Pavlas, Martin, 2019. "Bulky waste for energy recovery: Analysis of spatial distribution," Energy, Elsevier, vol. 181(C), pages 827-839.
    15. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).

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