Diagnosis and Prediction for Loss of Coolant Accidents in Nuclear Power Plants Using Deep Learning Methods

A combination of Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and LSTM (ConvLSTM) is constructed in this work for the fault diagnosis post-accident prediction Loss Coolant Accidents (LOCAs) Nuclear Power Plants (NPPs). The advantages ConvLSTM, such as effective feature determination extraction, are applied to classification LOCA cases. accuracy enhanced via collaborative CNN LSTM. Such a hybrid model proved be functional, accurate, adaptive, offering quick accident judgment reliable decision basis emergency response purpose. It then allows NPPs have an Artificial Intelligence (AI)-based solution prediction.