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Proceedings Paper

Using modular neural networks to monitor accident conditions in nuclear power plants
Author(s): Zhichao Guo; Robert E. Uhrig
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Paper Abstract

Nuclear power plants are very complex systems. The diagnoses of transients or accident conditions is very difficult because a large amount of information, which is often noisy, or intermittent, or even incomplete, needs to be processed in real time. To demonstrate their potential application to nuclear power plants, neural networks are used to monitor the accident scenarios simulated by the training simulator of TVA's Watts Bar Nuclear Power Plant. A self-organization network is used to compress original data to reduce the total number of training patterns. Different accident scenarios are closely related to different key parameters which distinguish one accident scenario from another. Therefore, the accident scenarios can be monitored by a set of small size neural networks, called modular networks, each one of which monitors only one assigned accident scenario, to obtain fast training and recall. Sensitivity analysis is applied to select proper input variables for modular networks.

Paper Details

Date Published: 16 September 1992
PDF: 12 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140029
Show Author Affiliations
Zhichao Guo, Univ. of Tennessee/Knoxville (United States)
Robert E. Uhrig, Univ. of Tennessee/Knoxville (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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