Development of a surrogate system of a plant dynamics simulation model and an abnormal situation identification system for nuclear power plants using deep neural networks

Seki, Akiyuki   ; Yoshikawa, Masanori ; Nishinomiya, Ryota*; Okita, Shoichiro  ; Takaya, Shigeru  ; Yan, X. 

Two types of deep neural network (DNN) systems have been constructed with the intent to assist safety operation of a nuclear power plant. One is a surrogate system (SS) that can estimate physical quantities of a nuclear power plant in a computational time of several orders less than a physical simulation model. The other is an abnormal situation identification system (ASIS) that can estimate the state of the disturbance causing an anomaly from physical quantities of a nuclear power plant. Both systems are trained and tested using data obtained from the analytical code for incore and plant dynamics (ACCORD), which reproduces the steady and dynamic behavior of the actual high Temperature engineering test reactor (HTTR) under various scenarios. The DNN models are built by adjusting, the main hyperparameters. Through these procedures, these systems are shown able to perform with a high degree of accuracy.