Abstract
The on-line signal analysis system designed for a multi-level mode operation using neural networks is described. The system is capable of monitoring the plant states by tracking different number of signals up to 32 simultaneously. The data used for this study were acquired from the Borssele Nuclear Power Plant (PWR type), and using the on-line monitoring system. An on-line plant-wide monitoring study using a multilayer neural network model is discussed in this paper. The back-propagation neural network algorithm is used for training the network. The technique assumes that each physical state of the power plant can be represented by a unique pattern of instrument readings which can be related to the condition of the plant. When disturbance occurs, the sensor readings undergo a transient, and form a different set of patterns which represent the new operational status. Diagnosing these patterns can be helpful in identifying this new state of the power plant. To this end, plant-wide monitoring with neutral networks is one of the new techniques in real-time applications. (author). 9 refs.; 5 figs.
Turkcan, E;
[1]
Ciftcioglu, O;
[2]
Eryurek, E;
Upadhyaya, B R
[3]
- Netherlands Energy Research Foundation (ECN), Petten (Netherlands)
- Istanbul Technical University (Turkey). Electrical Engineering Faculty
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
Citation Formats
Turkcan, E, Ciftcioglu, O, Eryurek, E, and Upadhyaya, B R.
On-line plant-wide monitoring using neural networks. Paper submitted to 8th Power Plant Dynamics, Control and Testing Symposium, May 27-29, 1992, Knoxville, Tennessee, USA.
Netherlands: N. p.,
1992.
Web.
Turkcan, E, Ciftcioglu, O, Eryurek, E, & Upadhyaya, B R.
On-line plant-wide monitoring using neural networks. Paper submitted to 8th Power Plant Dynamics, Control and Testing Symposium, May 27-29, 1992, Knoxville, Tennessee, USA.
Netherlands.
Turkcan, E, Ciftcioglu, O, Eryurek, E, and Upadhyaya, B R.
1992.
"On-line plant-wide monitoring using neural networks. Paper submitted to 8th Power Plant Dynamics, Control and Testing Symposium, May 27-29, 1992, Knoxville, Tennessee, USA."
Netherlands.
@misc{etde_10129479,
title = {On-line plant-wide monitoring using neural networks. Paper submitted to 8th Power Plant Dynamics, Control and Testing Symposium, May 27-29, 1992, Knoxville, Tennessee, USA}
author = {Turkcan, E, Ciftcioglu, O, Eryurek, E, and Upadhyaya, B R}
abstractNote = {The on-line signal analysis system designed for a multi-level mode operation using neural networks is described. The system is capable of monitoring the plant states by tracking different number of signals up to 32 simultaneously. The data used for this study were acquired from the Borssele Nuclear Power Plant (PWR type), and using the on-line monitoring system. An on-line plant-wide monitoring study using a multilayer neural network model is discussed in this paper. The back-propagation neural network algorithm is used for training the network. The technique assumes that each physical state of the power plant can be represented by a unique pattern of instrument readings which can be related to the condition of the plant. When disturbance occurs, the sensor readings undergo a transient, and form a different set of patterns which represent the new operational status. Diagnosing these patterns can be helpful in identifying this new state of the power plant. To this end, plant-wide monitoring with neutral networks is one of the new techniques in real-time applications. (author). 9 refs.; 5 figs.}
place = {Netherlands}
year = {1992}
month = {Jun}
}
title = {On-line plant-wide monitoring using neural networks. Paper submitted to 8th Power Plant Dynamics, Control and Testing Symposium, May 27-29, 1992, Knoxville, Tennessee, USA}
author = {Turkcan, E, Ciftcioglu, O, Eryurek, E, and Upadhyaya, B R}
abstractNote = {The on-line signal analysis system designed for a multi-level mode operation using neural networks is described. The system is capable of monitoring the plant states by tracking different number of signals up to 32 simultaneously. The data used for this study were acquired from the Borssele Nuclear Power Plant (PWR type), and using the on-line monitoring system. An on-line plant-wide monitoring study using a multilayer neural network model is discussed in this paper. The back-propagation neural network algorithm is used for training the network. The technique assumes that each physical state of the power plant can be represented by a unique pattern of instrument readings which can be related to the condition of the plant. When disturbance occurs, the sensor readings undergo a transient, and form a different set of patterns which represent the new operational status. Diagnosing these patterns can be helpful in identifying this new state of the power plant. To this end, plant-wide monitoring with neutral networks is one of the new techniques in real-time applications. (author). 9 refs.; 5 figs.}
place = {Netherlands}
year = {1992}
month = {Jun}
}