NUCLEAR-POWER-PLANT STATUS DIAGNOSTICS USING AN ARTIFICIAL NEURAL NETWORK

In this work, nuclear power plant operating status recognition is investigated using a self-optimizing stochastic learning algorithm artificial neural network (ANN) with dynamic node architecture learning. The objective is to train the ANN to classify selected nuclear power plant accident conditions and assess the potential for future success in this area. The network is trained on normal operating conditions as well as on potentially unsafe conditions based on nuclear power plant training simulator-generated accident scenarios. These scenarios include hot- and cold-leg loss of coolant, control rod ejection, total loss of off-site power, main steamline break, main feedwater line break, and steam generator tube leak accidents as well as the normal operating condition. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results. Continued research work is indicated.