Real-time Nuclear Power Plant Monitoring with Neural Network

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This paper addresses how to utilize artificial neural networks (ANNs) for detecting anomalies of nuclear power plants in operation. The basic principle of this methodology is to detect the anomaly with deviation between process signals measured from the actual plant and the corresponding output signals from the plant model, which is developed using three-layered auto-associative ANN; the auto-associativity has the advantage of detecting unknown plant conditions. A new learning technique adopted here compensates for the drawback of the conventional backpropagation algorithm, and is presented to make plant dynamic models on the ANN. The test results showed that this plant monitoring system is successful in detecting the symptoms of small anomalies in real-time over the wide power range including start-up, shut-down and steady state operations.

収録刊行物

  • Journal of nuclear science and technology  

    Journal of nuclear science and technology 35(2), 93-100, 1998-02-25 

    Atomic Energy Society of Japan

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各種コード

  • NII論文ID(NAID)
    10002078928
  • NII書誌ID(NCID)
    AA00703720
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    00223131
  • NDL 記事登録ID
    4416229
  • NDL 雑誌分類
    ZM35(科学技術--物理学)
  • NDL 請求記号
    Z53-A460
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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