A Neural Networks Design Methodology for Detecting Loss of Coolant Accidents in Nuclear Power Plants

Abstract

Artificial intelligence methods have been successfully applied to monitor the safety of nuclear power plants (NPPs). One major safety issue of a NPP is the loss of a coolant accident (LOCA) which is caused by the occurrence of a large break in the inlet headers (IH) of a nuclear reactor. Neural networks can be trained on transient datasets of a NPP to detect LOCA of the NPP. However, the transient datasets exhibit big data characteristics and designing an optimised neural network by exhaustive training all possible neural network architectures on big data can be very time-consuming because there exist a large number of possible neural network architectures for big data. This work proposes a neural network (NN) design methodology in three stages to detect the break sizes of the IHs of a NPP. In stage one, an optimised 1-hidden layer multilayer perceptron (MLP) is obtained by training and testing a number of 1-hidden layer MLP architectures which are determined empirically. In stage two, a number of 2-hidden layer MLP architectures are determined based on the number of the weights of the optimised 1-hidden layer MLP; then, an optimised 2-hidden layer MLP is obtained by training and testing these 2-hidden layer MLP architectures. In stage three, the break sizes not present in the transient dataset are generated using linear interpolation method; then, the optimised 2-hidden layer MLP is trained and tested iteratively 100 times using the transient dataset added with the linear interpolation dataset. The results show that the proposed methodology outperformed the MLP of the previous work. Compared with exhaustive training of all 2-hidden layer architectures, the speed of the proposed methodology is faster than that of exhaustive training. Additionally, the optimised 2-hidden layer MLP of the proposed methodology has a similar performance to exhaustive training. We consider this work as an engineering application of predictive data analytics for which neural networks are used as the primary tool.