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Gross error isolability for operational data in power plants

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

Abstract

Power plant on-line measured operational data inevitably contain random and gross errors. Data reconciliation is a data preprocessing technique, which makes use of redundant measured data to reduce the effect of random errors, and identify gross errors together with a statistical test method. When applying the data reconciliation based gross error identification method in real-life process, it is sometimes difficult to isolate a small magnitude gross error in one measurement from another due to the influence of system constraint nature and random errors. As a result, the magnitude of a gross error should satisfy a quantitative criterion to make sure of its sufficient isolation from other measurements. In this work, we propose a mathematical method to evaluate the minimum isolable magnitude for a gross error in one measurement to be isolated from another with a required probability for data reconciliation based gross error identification. We also illustrate an application of the proposed method to the feed water regenerative heating system in a 1000 MW ultra-supercritical coal-fired power generation unit. Validation of the proposed method through simulation studies is also provided, together with the influence of system constraint nature and random error standard deviations on the gross error minimum isolable magnitudes.

Suggested Citation

  • Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Gross error isolability for operational data in power plants," Energy, Elsevier, vol. 74(C), pages 918-927.
    • Handle: RePEc:eee:energy:v:74:y:2014:i:c:p:918-927
    DOI: 10.1016/j.energy.2014.07.071
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    References listed on IDEAS

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    1. 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.
    2. Martini, A. & Sorce, A. & Traverso, A. & Massardo, A., 2013. "Data Reconciliation for power systems monitoring: Application to a microturbine-based test rig," Applied Energy, Elsevier, vol. 111(C), pages 1152-1161.
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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. 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. 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.
    5. 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.
    6. 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.
    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).
    8. 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).
    9. 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.
    10. 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.
    11. 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).
    12. 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.

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