Temporal neural network for the identification of nuclear power pant transients

O. Uluyol, Magdi Ragheb

Research output: Contribution to journalArticlepeer-review

Abstract

A layered spatiotemporal neutral network is proposed for the identification of nuclear power plant transients. The developed layered spatiotemporal network is inspired by the formal avalanche structure developed by S. Grossberg and offers advantages compared with the stationary pattern approach using the perceptron paradigm. Each layer in the network is trained to recognize a separate time-dependent accident scenario. Within each scenario, the temporal behavior of the revelant parameters such as pressurizer pressure, pressurizer water volume, cold and hot legs temperatures, vessel flow, and power, are considered. Numerical, vessel flow, and power, are considered where the proposed methodology is applied to two nuclear power plant anticipated transient scenarios: the Station Blackout and the Anticipated Transient without Scram transients in a pressurized water reactor.

Original languageEnglish (US)
Pages (from-to)854-859
Number of pages6
JournalProceedings of the American Power Conference
Volume55
Issue numberpt 1
StatePublished - 1993

ASJC Scopus subject areas

  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering

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