Data reconciliation and gross error detection for operational data in power plants

Xiaolong Jiang, Pei Liu and Zheng Li

Energy, 2014, vol. 75, issue C, 14-23

Abstract: The quality of on-line measured operational data is usually not satisfactory for the performance monitoring of coal-fired power plants, due to the low accuracy of measuring instrument. Data reconciliation is a data preprocessing technique which can improve the accuracy of measured data through process modeling and optimization, and can also be used for gross error detection together with a statistical test method. In this work, we provide a mathematical framework for gross error detection in power plants via data reconciliation. We also provide case studies to implement the proposed framework in the feed water regenerative heating system of a real-life 1000 MW ultra-supercritical coal-fired power generation unit. Data reconciliation simulation results show that the relative root mean squared errors of the primary flow measurements, namely the outlet flow rate of the #1 feed water heater, the outlet flow rate of the feed water pump, and the inlet flow rate of condensate water in the deaerator are reduced by 72%, 40%, 22%. Simulation results also show that data reconciliation is effective for accuracy improvement when estimated error standard deviations are different from the actual ones and when random errors follow generalized normal distributions. We then provide a case study where gross error detection is performed together with a global test and a serial elimination strategy, and a gross error in the measurement of outlet flow rate of the feed water pump is successfully detected and validated by the on-site inspection and maintenance records of the power plant.

Keywords: Power plant; Data reconciliation; Gross error detection (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544214002862
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:75:y:2014:i:c:p:14-23

DOI: 10.1016/j.energy.2014.03.024

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().