A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants
نویسندگان
چکیده
Operators in the main control room of a nuclear power plant have crucial role supervising all operations, and any human error can be fatal. By providing operators with information regarding future trends safety-critical parameters based on their actions, errors detected prevented timely manner. This paper proposed Sequence-to-Sequence (Seq2Seq)-based Long Short-Term Memory (LSTM) model to predict trends. The PCTran was used extract data for four typical faults fault levels, eighty-six were selected as characteristic quantities. training, validation, testing sets collected ratio 13:3:1, appropriate hyperparameters construct Seq2Seq neural network. Compared conventional deep learning models, results indicated that could successfully solve complex problem trend estimation key system under influence operator action factors multiple abnormal operating conditions. It is believed help reduce risk human-caused diagnose potential accidents.
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ژورنال
عنوان ژورنال: Sustainability
سال: 2023
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su15076310