Intelligent support system to diagnose abnormal states of nuclear power plants

Jae Min Kim, Gyumin Lee, Seung Jun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nuclear power plants have abnormal operating procedures to prepare abnormal events occurring. An operator should choose and follow the appropriate procedure according to alarms and plant parameters which indicate the plant state. However, with enormous information, it is sometimes hard for the operators to judge the plant state in a short period of time. In the field, the skilled operators are well trained in the entry conditions of the abnormal operating procedures, so that they can quickly select a procedure that is appropriate to the current situation. Nevertheless, this task has a potential risk for less skilled operators to make mistakes of the judgement, which would result in response time delayed. Therefore, this paper suggests nuclear power plants abnormality diagnosis algorithm to support the judgement. This paper covers two of three steps to develop the diagnosis system; setting the training data production environment by analyzing the abnormal operating procedures and comparison between deep learning algorithms using the convolutional and recurrent neural networks. The abnormal operating data were generated from the nuclear power plant simulator. In addition, to reduce the dimensionality of the data, principal component analysis was used as data preprocessing. The algorithm is expected to reduce work load of the operators by providing selection of the proper procedure in a short time with high accuracy.

Original languageEnglish
Title of host publication11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019
PublisherAmerican Nuclear Society
Pages422-430
Number of pages9
ISBN (Electronic)9780894487835
StatePublished - 2019
Event11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019 - Orlando, United States
Duration: 9 Feb 201914 Feb 2019

Publication series

Name11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019

Conference

Conference11th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2019
Country/TerritoryUnited States
CityOrlando
Period9/02/1914/02/19

Keywords

  • Abnormal operating procedure
  • Deep learning
  • Diagnosis
  • Neural network
  • Nuclear power plant

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