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 language | English |
|---|---|
| Article number | 012019 |
| Journal | IOP Conference Series: Earth and Environmental Science |
| Volume | 16 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 01 Jan 2013 |
| Event | 26th IAHR Symposium on Hydraulic Machinery and Systems - Beijing, China Duration: 19 Aug 2012 → 23 Aug 2012 |
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All Science Journal Classification (ASJC) codes
- Environmental Science(all)
- Earth and Planetary Sciences(all)
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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 journal › Conference 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
PY - 2013/1/1
Y1 - 2013/1/1
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»
UR - http://www.scopus.com/inward/record.url?scp=84881111291&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881111291&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/16/1/012019
DO - 10.1088/1755-1315/16/1/012019
M3 - Conference article
AN - SCOPUS:84881111291
VL - 16
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
SN - 1755-1307
IS - 1
M1 - 012019
ER -