Automatic Planning of Electrical Isolation with Deep Learning in Nuclear Power Plant

Abstract

We study automatic planning of electrical isolation with deep learning, one of applications of artificial intelligence to enhance operation and maintenance. Currently, a skilled engineer plans electrical isolation procedure with hundreds of the circuit diagrams and the related documents, taking man-hours. If this task becomes automatic, it is very efficient. A major issue of the automatic planning is much calculation time of electrical circuit simulator, searching billions of electrical conducting paths. We performed a simplified case study of the electrical isolation. We applied a deep neural network (DNN) for dropping in the calculation time. We trained the responses of the circuit simulator to the DNN, constructing an optimized path search algorithm in the DNN. The calculation time of the DNN was shorter by a factor of 560, compared with that of the electrical circuit simulator. There was no significant difference in accuracy between Multi-Layer Perceptron and Graph Convolutional Network.