Diagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods - She, Jingke; Shi, Tianzi; Xue, Shiyu; Zhu, Yan; Lu, Shaofei; Sun, Peiwei; Cao, Huasong (Frontiers in Energy Research, 2021)
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.