Nuclear power plant status diagnostics using an artificial neural network
Abstract
In this paper, nuclear power plant operating status recognition is investigated using a self-optimizing stochastic learning algorithm artificial neutral network (ANN) with dynamic node architecture learning. The objective is to train the ANN to classify selected nuclear power plant accident conditions and assess the potential for future success in this area. The network is trained on normal operating conditions as well as on potentially unsafe conditions based on nuclear power plant training simulator-generated accident scenarios. These scenarios include hot-and cold-leg loss of coolant, control rod ejection, total loss of off-site power, main streamline break, main feedwater line break, and steam generator tube leak accidents as well as the normal operating condition. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results. continued research work indicated.
- Authors:
-
- Univ. of Tennessee-Knoxville, Dept. of Nuclear Engineering, Knoxville, TN (US)
- Publication Date:
- OSTI Identifier:
- 5402772
- Resource Type:
- Journal Article
- Journal Name:
- Nuclear Technology; (United States)
- Additional Journal Information:
- Journal Volume: 97:3; Journal ID: ISSN 0029-5450
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 22 GENERAL STUDIES OF NUCLEAR REACTORS; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; DIAGNOSTIC TECHNIQUES; ALGORITHMS; NUCLEAR POWER PLANTS; ARTIFICIAL INTELLIGENCE; LEAKS; LOSS OF COOLANT; OPERATION; OPTIMIZATION; REACTOR ACCIDENTS; STOCHASTIC PROCESSES; ACCIDENTS; MATHEMATICAL LOGIC; NUCLEAR FACILITIES; POWER PLANTS; THERMAL POWER PLANTS; 220900* - Nuclear Reactor Technology- Reactor Safety; 990200 - Mathematics & Computers
Citation Formats
Bartlett, E B, and Uhrig, R E. Nuclear power plant status diagnostics using an artificial neural network. United States: N. p., 1992.
Web.
Bartlett, E B, & Uhrig, R E. Nuclear power plant status diagnostics using an artificial neural network. United States.
Bartlett, E B, and Uhrig, R E. 1992.
"Nuclear power plant status diagnostics using an artificial neural network". United States.
@article{osti_5402772,
title = {Nuclear power plant status diagnostics using an artificial neural network},
author = {Bartlett, E B and Uhrig, R E},
abstractNote = {In this paper, nuclear power plant operating status recognition is investigated using a self-optimizing stochastic learning algorithm artificial neutral network (ANN) with dynamic node architecture learning. The objective is to train the ANN to classify selected nuclear power plant accident conditions and assess the potential for future success in this area. The network is trained on normal operating conditions as well as on potentially unsafe conditions based on nuclear power plant training simulator-generated accident scenarios. These scenarios include hot-and cold-leg loss of coolant, control rod ejection, total loss of off-site power, main streamline break, main feedwater line break, and steam generator tube leak accidents as well as the normal operating condition. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results. continued research work indicated.},
doi = {},
url = {https://www.osti.gov/biblio/5402772},
journal = {Nuclear Technology; (United States)},
issn = {0029-5450},
number = ,
volume = 97:3,
place = {United States},
year = {Sun Mar 01 00:00:00 EST 1992},
month = {Sun Mar 01 00:00:00 EST 1992}
}