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New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants

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  • Blanco, J.M.
  • Vazquez, L.
  • Peña, F.
  • Diaz, D.

Abstract

One of the main aspects in energy conversion systems is to identify which time segment of instrumental recorded measurements allows accurate characterization of the system operation mode under a so-called “quasi steady-state”. In this paper, a diagnosis procedure on an existing steam generator operating at base load in a reference power plant has been improved. The setting points for a group of key variables were considered as reference values. To assess the effects of further deviations during the time segments of operation, a set of reference variables estimated fuel overconsumption levels with regard to a theoretical zero deviation. The appropriate combination of the above mentioned regulated outputs, together with a set of suggested key modules, allowed the careful building of variants of tailor-made enhanced developments for diagnosis proposals. Finally, the contribution of this study to the assessment of compliance with environmental regulations was achieved, showing relevant savings in terms of energy consumption.

Suggested Citation

  • Blanco, J.M. & Vazquez, L. & Peña, F. & Diaz, D., 2013. "New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants," Applied Energy, Elsevier, vol. 101(C), pages 589-599.
    • Handle: RePEc:eee:appene:v:101:y:2013:i:c:p:589-599
    DOI: 10.1016/j.apenergy.2012.06.060
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    References listed on IDEAS

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    Cited by:

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    2. Liping Wang & Minghao Liu & Boquan Wang & Jiajie Wu & Chuangang Li, 2017. "Study on Nested-Structured Load Shedding Method of Thermal Power Stations Based on Output Fluctuations," Energies, MDPI, vol. 10(10), pages 1-16, September.
    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. 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.
    5. 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.
    6. Xu, Jing & Bi, Dapeng & Ma, Suxia & Bai, Jin, 2020. "A data-based approach for benchmark interval determination with varying operating conditions in the coal-fired power unit," Energy, Elsevier, vol. 211(C).
    7. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    8. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).

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