NEURO-FUZZY SYSTEM TO SUPPORT THE ATTENTION AND DIRECTION OF NUCLEAR POWER PLANT OPERATORS

Accident diagnosis in nuclear power plants (NPPs) is a very hard task for plant operators due to the number of variables they have to deal simultaneously when facing accident situations. The previous identification of possible accident situations is an essential issue for safe operation in NPPs. Artificial intelligence techniques and tools are suitable to identify complex systems accident situations because the system faults and anomalies lead to different pattern evolution in the correlated processes variables. Such patterns can be identified by Artificial Neuron Networks (ANNs). The system developed in this work aims to support operators' attention and direction during accidents in NPPs using a Neuro-Fuzzy approach for event's identification forecast. ANNs are used to perform this task. After the NN has done the event type identification, a fuzzy-logic system analyzes the results giving a reliability level of that. The results have shown the system is capable to help the operators to direct their attention and narrow their information search field in the noisy background of the operation during accident situations in nuclear power plants.