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A data reconciliation based framework for integrated sensor and equipment performance monitoring in power plants

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

Abstract

Power plant on-line measured operational data are often corrupted with random and gross errors. The data reconciliation method can reduce the impact of random errors by adjusting redundant measurements to satisfy system constraints and detect gross errors together with a statistical test method. In previous studies, the data reconciliation method is mainly used to deal with measurements with random and gross errors, and its application is mainly in the data preprocessing areas. In this work, we extend the data reconciliation and gross error detection method to cover both sensor and equipment performance monitoring in power plants, through introducing equipment characteristic constraints together with characteristic parameter dominant factor models in the data reconciliation method. The validity and capability of the proposed framework are illustrated with case studies in the feed water regenerative heating system of a 1000MW ultra-supercritical coal-fired power generation unit. Case study results show that the characteristic parameter dominant factor models have relative root mean squared errors smaller than 2.3%, whilst the distribution properties of the test statistics for the integrated sensor and equipment performance monitoring are validated with simulated test statistic samples. We also illustrate that the proposed framework can efficiently detect and identify both sensor biases and equipment faults in the system. At the same time, the ability of the data reconciliation and global test method for measurement gross error detection is also improved due to the increased system redundancy under the proposed framework.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:134:y:2014:i:c:p:270-282
    DOI: 10.1016/j.apenergy.2014.08.040
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    Cited by:

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    6. Syed, Mohammed S. & Dooley, Kerry M. & Madron, Frantisek & Knopf, F. Carl, 2016. "Enhanced turbine monitoring using emissions measurements and data reconciliation," Applied Energy, Elsevier, vol. 173(C), pages 355-365.
    7. 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).
    8. Yongping Yang & Xiaoen Li & Zhiping Yang & Qing Wei & Ningling Wang & Ligang Wang, 2018. "The Application of Cyber Physical System for Thermal Power Plants: Data-Driven Modeling," Energies, MDPI, vol. 11(4), pages 1-16, March.

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