• DocumentCode
    836533
  • Title

    Nuclear power plant performance study by using neural networks

  • Author

    Guo, Zhichao ; Uhrig, Robert E.

  • Author_Institution
    Dept. of Nucl. Eng., Tennessee Univ., Knoxville, TN, USA
  • Volume
    39
  • Issue
    4
  • fYear
    1992
  • fDate
    8/1/1992 12:00:00 AM
  • Firstpage
    915
  • Lastpage
    918
  • Abstract
    The thermal performance data, obtained from Tennessee Valley Authority (TVA) Sequoyah nuclear power plant showed that the heat rate was changing constantly and the power was probably losing some megawatts of electric power due to the variation of the heat rate. The model of the thermodynamic process was obtained using a neural network trained on actual measurements from the plant over a one-year period of time. The model represented the thermodynamic process as it actually existed in the plant, and the dynamic range of the data covered the normal range of variables during a typical annual cycle. A sensitivity study was applied to the neural network model to extract information about the key parameters which might strongly affect the plant thermal performance
  • Keywords
    neural nets; nuclear engineering computing; Sequoyah nuclear power plant; Tennessee Valley Authority; annual cycle; dynamic range; heat rate; neural networks; thermal performance data; Backpropagation; Expert systems; Heat engines; Neural networks; Nuclear measurements; Power generation; Power measurement; Power system modeling; Thermal variables measurement; Thermodynamics;
  • fLanguage
    English
  • Journal_Title
    Nuclear Science, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9499
  • Type

    jour

  • DOI
    10.1109/23.159732
  • Filename
    159732