Abstract
A combination of Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) is constructed in this work for the fault diagnosis and post-accident prediction for Loss of Coolant Accidents (LOCAs) in Nuclear Power Plants (NPPs). The advantages of ConvLSTM, such as effective feature determination and extraction, are applied to the classification of LOCA cases. The prediction accuracy is enhanced via the collaborative work of CNN and LSTM. Such a hybrid model is proved to be functional, accurate, and adaptive, offering quick accident judgment and a reliable decision basis for the emergency response purpose. It then allows NPPs to have an Artificial Intelligence (AI)-based solution for fault diagnosis and post-accident prediction.
Highlights
The quick and accurate response to a Nuclear Power Plants (NPP) accident is critical to the safety of both the plant and the public
A hybrid model for Loss of Coolant Accidents (LOCAs) diagnosis and prediction is proposed in this work
The ConvLSTM is used for fault type diagnosis, and the LOCA prediction is produced using Convolutional Neural Network (CNN)-Long-Short Term Memory (LSTM)
Summary
The quick and accurate response to a Nuclear Power Plants (NPP) accident is critical to the safety of both the plant and the public. The training dataset from such platform enables the ConvLSTM model to recognize features of different break sizes such that the LOCA type can be confirmed at an early stage of the accident. LSTM, as a deep learning model for long-time series prediction, is utilized to calculate the post-LOCA development of critical system parameters.
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