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자료유형
학술저널
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한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제50권 제4호
발행연도
2018.1
수록면
582 - 588 (7page)

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Accident diagnosis is one of the complex tasks for nuclear power plant (NPP) operators. In abnormal oremergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address thisproblem. Among them, the recurrent neural network (RNN) has performed well at analyzing time seriesdata. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM),which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists ofpreprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed inputvariables are calculated to output the accident diagnosis results. The outputs are also postprocessedusing softmax to determine the ranking of accident diagnosis results with probabilities. This algorithmwas trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steamgenerator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstratethe feasibility of diagnosing NPP accidents.

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