Prediction of Major Transient Scenarios for Severe Accidents of Nuclear Power Plants

Abstract

It is very difficult for nuclear power plant operators to predict and identify the major severe accident scenarios following an initiating event by staring at temporal trends of important parameters. In this regard, a probabilistic neural network (PNN) that has been applied well to the classification problems is used in order to classify accidents into groups of initiating events such as loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR). Also, a fuzzy neural network (FNN) is designed to identify their major severe accident scenarios after the initiating events. The inputs to PNN and FNN are initial time-integrated values obtained by integrating measurement signals during a short time interval after reactor scram. An automatic structure constructor for the fuzzy neural network automatically selects the input variables from the time-integrated values of many measured signals, and optimizes the number of rules and its related parameters. In cases that an initiating event develops into a severe accident, this may happen when plant operators do not follow the appropriate accident management guidance or plant safety systems do not work, the proposed algorithm showed accurate classification of initiating events. Also, it well predicted timings for important occurrences during severe accident progression scenarios, which is very helpful to perform severe accident management.