IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v165y2016icp1037-1051.html
   My bibliography  Save this article

Data reconciliation for the overall thermal system of a steam turbine power plant

Author

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

Abstract

Accuracy of online measurement data is usually not satisfactory for coal-fired power plants due to constraints of measurement techniques. Data reconciliation can help to improve the accuracy of online measurement with no extra requirements on equipment upgrading. Traditionally, data reconciliation is applied to a sub-system of a coal-fired power plant, for instance, mass balance of a steam turbine system. In this work, we present a systematic approach where data reconciliation is applied to the overall thermal system of a real-life steam turbine power plant. Improvement of data accuracy is obtained via the proposed approach compared with sub-system studies. Optimization of system selection and configuration of a data reconciliation problem is analyzed. Results show that uncertainty of on-line measured data can be reduced by up to 50% in a 1000MW ultra-supercritical coal-fired steam turbine power plant compared with previous studies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:165:y:2016:i:c:p:1037-1051
    DOI: 10.1016/j.apenergy.2016.01.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916000131
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.01.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Bonvini, Marco & Sohn, Michael D. & Granderson, Jessica & Wetter, Michael & Piette, Mary Ann, 2014. "Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques," Applied Energy, Elsevier, vol. 124(C), pages 156-166.
    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. Nikpey, H. & Assadi, M. & Breuhaus, P. & Mørkved, P.T., 2014. "Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas," Applied Energy, Elsevier, vol. 117(C), pages 30-41.
    5. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Gross error isolability for operational data in power plants," Energy, Elsevier, vol. 74(C), pages 918-927.
    6. 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.
    7. Usón, Sergio & Valero, Antonio & Correas, Luis, 2010. "Energy efficiency assessment and improvement in energy intensive systems through thermoeconomic diagnosis of the operation," Applied Energy, Elsevier, vol. 87(6), pages 1989-1995, June.
    8. Finn, Joshua & Wagner, John & Bassily, Hany, 2010. "Monitoring strategies for a combined cycle electric power generator," Applied Energy, Elsevier, vol. 87(8), pages 2621-2627, August.
    9. 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.
    10. Szega, Marcin & Nowak, Grzegorz Tadeusz, 2015. "An optimization of redundant measurements location for thermal capacity of power unit steam boiler calculations using data reconciliation method," Energy, Elsevier, vol. 92(P1), pages 135-141.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. Husnul Qodim & Herningsih Herningsih & Phong Thanh Nguyen & Quyen Le Hoang Thuy To Nguyen & Apriana Toding, 2019. "Educating the Information Integration Using Contextual Knowledge and Ontology Merging in Advanced Levels," International Journal of Higher Education, Sciedu Press, vol. 8(8), pages 1-24, December.
    4. 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.
    5. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    6. 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).
    7. Wang, Maojian & Liu, Guilian & Hui, Chi Wai, 2017. "Novel shortcut optimization model for regenerative steam power plant," Energy, Elsevier, vol. 138(C), pages 529-541.
    8. Oleg Todorov & Kari Alanne & Markku Virtanen & Risto Kosonen, 2021. "A Novel Data Management Methodology and Case Study for Monitoring and Performance Analysis of Large-Scale Ground Source Heat Pump (GSHP) and Borehole Thermal Energy Storage (BTES) System," Energies, MDPI, vol. 14(6), pages 1-25, March.
    9. 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).
    10. Michał Kozioł & Joachim Kozioł, 2021. "Application of Data Validation and Reconciliation to Improve Measurement Results in the Determination Process of Emission Characteristics in Co-Combustion of Sewage Sludge with Coal," Sustainability, MDPI, vol. 13(9), pages 1-19, May.
    11. 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).
    12. Li, Xiaoen & Wang, Ningling & Wang, Ligang & Yang, Yongping & Maréchal, François, 2018. "Identification of optimal operating strategy of direct air-cooling condenser for Rankine cycle based power plants," Applied Energy, Elsevier, vol. 209(C), pages 153-166.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    8. 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.
    9. Blanco, Jesús M. & Vazquez, L. & Peña, F., 2012. "Investigation on a new methodology for thermal power plant assessment through live diagnosis monitoring of selected process parameters; application to a case study," Energy, Elsevier, vol. 42(1), pages 170-180.
    10. 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.
    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. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Gross error isolability for operational data in power plants," Energy, Elsevier, vol. 74(C), pages 918-927.
    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. Plis, Marcin & Rusinowski, Henryk, 2019. "Identification of mathematical models of thermal processes with reconciled measurement results," Energy, Elsevier, vol. 177(C), pages 192-202.
    15. 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.
    16. 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.
    17. Szega, Marcin, 2017. "An improvement of measurements reliability in thermal processes by application of the advanced data reconciliation method with the use of fuzzy uncertainties of measurements," Energy, Elsevier, vol. 141(C), pages 2490-2498.
    18. 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).
    19. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:165:y:2016:i:c:p:1037-1051. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.