Improved WaveNet for pressurized water reactor accident prediction

Many studies have proposed deep learning models to diagnose faults and predict accidents in nuclear power reactors. However, the training data in these studies are deterministic, and neglect the parameter uncertainty caused by sensor failure in accident scenario. Developing a robust deep learning model requires consideration for uncertainties and possible malfunctions of the sensors. Consequently, this work presents a novel application of wave network (WaveNet) architecture for pressurized water reactor accident prediction using an augmented dataset that reflects the sensor status during accidents. The WaveNet is developed with stacked dilated convo-lution networks with non-causal layers. The dilated convolution is separated by LeakyRelu and Batch Normal-ization layers. A flatten layer is added before the output layer to reduce the three-dimensional convolution output to 2D output. Two dense layers are placed at the top to output the model prediction. In this study, four reactor accidents (LOCA, steam generator tube rupture, loss of AC power, and steam line break inside containment) are simulated with twelve augmented reactor coolant parameters used to train and test the model's predictive performance. The model performance is evaluated using the inference time accuracy, precision, recall, and the F1 score. Different model architecture is also evaluated to determine the best model hyperparameters for accurate prediction of reactor accidents. Finally, the predictive capability of the WaveNet model is compared with the convolution long short-term memory (CNN-LSTM) model. The result shows that the best WaveNet architecture predicts the simulated accidents with an accuracy of 99.3%.