IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v111y2013icp1152-1161.html
   My bibliography  Save this article

Data Reconciliation for power systems monitoring: Application to a microturbine-based test rig

Author

Listed:
  • Martini, A.
  • Sorce, A.
  • Traverso, A.
  • Massardo, A.

Abstract

In this study the techniques of Data Reconciliation and Gross Error Detection have been applied to a microturbine-based test rig installed at the Thermochemical Power Group (TPG) laboratory of the University of Genoa, Italy. These techniques have been developed in the field of chemical engineering during the past 55years with the purpose of reducing the effect of random errors and also eliminating systematic gross errors in the data by exploiting the relationships that are known to exist between different variables of a process (e.g. energy and mass balances). Two different applications of Data Reconciliation have been carried out: first the entire test rig was studied, generating a set of measurements affected just by random error; second, only the recuperator was taken into account using real measurements coming from a steady state test performed on the plant. The purpose of the former is to show the capability of Data Reconciliation in the adjustment of the measurements so that they can respect the constraints (balance equations). This application is somehow an “ideal application” of Data Reconciliation. Latter since gross errors are often present in the measurements coming from a real plant, a “real or experimental application” of Data Reconciliation was considered for a subsystem of the plant in which the actual measurements from plant probes were used. The objective was to understand if the measured values of temperature, pressure and mass flow rate at the inlet and outlet of the recuperator were physically compatible and reliable. Measured value affected by gross error was identified, focusing on its effect over all the other measurements during DR calculation.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:1152-1161
    DOI: 10.1016/j.apenergy.2012.12.045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261912009361
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2012.12.045?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.

    Citations

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


    Cited by:

    1. 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.
    2. Duan, Jiandong & Liu, Junjie & Xiao, Qian & Fan, Shaogui & Sun, Li & Wang, Guanglin, 2019. "Cooperative controls of micro gas turbine and super capacitor hybrid power generation system for pulsed power load," Energy, Elsevier, vol. 169(C), pages 1242-1258.
    3. 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.
    4. 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.
    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. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Gross error isolability for operational data in power plants," Energy, Elsevier, vol. 74(C), pages 918-927.
    7. 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.
    8. Duan, Jiandong & Fan, Shaogui & Wu, Fengjiang & Sun, Li & Wang, Guanglin, 2017. "Power balance control of micro gas turbine generation system based on supercapacitor energy storage," Energy, Elsevier, vol. 119(C), pages 442-452.

    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:111:y:2013:i:c:p:1152-1161. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.