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Identification and isolability of multiple gross errors in measured data for power plants

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

Abstract

Raw measured data from an industrial process inherently contain measurement errors. Data reconciliation together with statistical test methods can be used for gross error detection and identification. The magnitude of a gross error should satisfy a quantitative criterion for sufficient isolation from other measurements. However, research on the isolability and identification for multiple gross errors and comparison with single gross error are rarely insufficient. In this work, a mathematical method for evaluating the identification and isolability of multiple gross errors is proposed, and case studies in a real-life 1000 MW coal-fired steam turbine power plant using measured data are carried out. The isolability of multiple gross errors are firstly analyzed theoretically, then examples of the absolute minimum isolable magnitudes for multiple gross errors are presented and validated. Besides, the impact of system redundancy on gross error isolability is also investigated. Results indicate that the minimum isolable magnitude of a gross error is larger in a system with larger redundancy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:177-187
    DOI: 10.1016/j.energy.2016.07.137
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. 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).
    2. 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).
    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.

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