Back propagation artificial neural network and its application in fault detection of condenser failure in thermo plant

Firas B. Ismail, Vinesh Thiruchelvam

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

Steam condenser is one of the most important equipment in steam power plants. If the steam condenser trips it may lead to whole unit shutdown, which is economically burdensome. Early condenser trips monitoring is crucial to maintain normal and safe operational conditions. In the present work, artificial intelligent monitoring systems specialized in condenser outages has been proposed and coded within the MATLAB environment. The training and validation of the system has been performed using real operational measurements captured from the control system of selected steam power plant. An integrated plant data preparation scheme for condenser outages with related operational variables has been proposed. Condenser outages under consideration have been detected by developed system before the plant control system»

Original languageEnglish
Article number012019
JournalIOP Conference Series: Earth and Environmental Science
Volume16
Issue number1
DOIs
Publication statusPublished - 01 Jan 2013
Event26th IAHR Symposium on Hydraulic Machinery and Systems - Beijing, China
Duration: 19 Aug 201223 Aug 2012

Fingerprint

back propagation
artificial neural network
control system
power plant
monitoring system
monitoring
detection

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)

Cite this

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title = "Back propagation artificial neural network and its application in fault detection of condenser failure in thermo plant",
abstract = "Steam condenser is one of the most important equipment in steam power plants. If the steam condenser trips it may lead to whole unit shutdown, which is economically burdensome. Early condenser trips monitoring is crucial to maintain normal and safe operational conditions. In the present work, artificial intelligent monitoring systems specialized in condenser outages has been proposed and coded within the MATLAB environment. The training and validation of the system has been performed using real operational measurements captured from the control system of selected steam power plant. An integrated plant data preparation scheme for condenser outages with related operational variables has been proposed. Condenser outages under consideration have been detected by developed system before the plant control system»",
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Back propagation artificial neural network and its application in fault detection of condenser failure in thermo plant. / Ismail, Firas B.; Thiruchelvam, Vinesh.

In: IOP Conference Series: Earth and Environmental Science, Vol. 16, No. 1, 012019, 01.01.2013.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Back propagation artificial neural network and its application in fault detection of condenser failure in thermo plant

AU - Ismail, Firas B.

AU - Thiruchelvam, Vinesh

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N2 - Steam condenser is one of the most important equipment in steam power plants. If the steam condenser trips it may lead to whole unit shutdown, which is economically burdensome. Early condenser trips monitoring is crucial to maintain normal and safe operational conditions. In the present work, artificial intelligent monitoring systems specialized in condenser outages has been proposed and coded within the MATLAB environment. The training and validation of the system has been performed using real operational measurements captured from the control system of selected steam power plant. An integrated plant data preparation scheme for condenser outages with related operational variables has been proposed. Condenser outages under consideration have been detected by developed system before the plant control system»

AB - Steam condenser is one of the most important equipment in steam power plants. If the steam condenser trips it may lead to whole unit shutdown, which is economically burdensome. Early condenser trips monitoring is crucial to maintain normal and safe operational conditions. In the present work, artificial intelligent monitoring systems specialized in condenser outages has been proposed and coded within the MATLAB environment. The training and validation of the system has been performed using real operational measurements captured from the control system of selected steam power plant. An integrated plant data preparation scheme for condenser outages with related operational variables has been proposed. Condenser outages under consideration have been detected by developed system before the plant control system»

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