Nuclear power plant status diagnostics using artificial neural networks
- Iowa State Univ. of Science and Technology, Ames, IA (United States). Dept. of Mechanical Engineering
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as 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, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.
- Research Organization:
- Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG07-88ER12824
- OSTI ID:
- 10104399
- Report Number(s):
- CONF-9109110-15; ON: DE93003560
- Resource Relation:
- Conference: International conference on frontiers in innovative computing for the nuclear industry,Jackson, WY (United States),15-18 Sep 1991; Other Information: PBD: [1991]
- Country of Publication:
- United States
- Language:
- English
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