Research on Simulation and State Prediction of Nuclear Power System Based on LSTM Neural Network

Yusheng Chen,Ren Yu,Tianshu Wang,Meng Lin,Arkady Serikov

doi:10.1155/2021/8839867

Abstract

The nuclear power plant systems are coupled with each other, and their operation conditions are changeable and complex. In the case of an operation fault in these systems, there will be a large number of alarm parameters, which can cause humans to be hurt in the accidents under great pressure. Therefore, it is necessary to predict the values of the key parameters of a device system. The prediction of the key parameters’ values can help operators determine the changing trends of system parameters in advance, which can effectively improve system safety. In this paper, a deep learning long short-term memory (LSTM) neural network model is developed to predict the key parameters of a nuclear power plant. The proposed network is verified by simulations and compared with the traditional grey theory. The simulation and comparison results show that the proposed LSTM neural network is effective and accurate in predicting the key parameters of the nuclear power plant.

Highlights

Academic Editor: Arkady Serikov e nuclear power plant systems are coupled with each other, and their operation conditions are changeable and complex
A deep learning long short-term memory (LSTM) neural network model is developed to predict the key parameters of a nuclear power plant. e proposed network is verified by simulations and compared with the traditional grey theory. e simulation and comparison results show that the proposed LSTM neural network is effective and accurate in predicting the key parameters of the nuclear power plant
The LSTM neural network method is used to study the prediction technology of nuclear power plant key parameters, and the LSTM model is improved. e target mechanism and bidirectional long short-term memory (BiLSTM) are combined to establish the data prediction model of the nuclear power plant based on BiLSTM-attention deep learning

Summary

Research Article

Research on Simulation and State Prediction of Nuclear Power System Based on LSTM Neural Network. A deep learning long short-term memory (LSTM) neural network model is developed to predict the key parameters of a nuclear power plant. E simulation and comparison results show that the proposed LSTM neural network is effective and accurate in predicting the key parameters of the nuclear power plant. Ese methods can predict the changing trend of the system’s operational parameters under different working conditions in real-time and, upgrade the safety control of nuclear power plants from the response after occurrence to the prediction and early intervention. A deep learning long short-term memory (LSTM) neural network is proposed to predict the key parameters of the nuclear power plant. When the pressurizer reaches a certain value, the pressurizer pressure is mainly regulated by the pressurizer pressure control system. e pressurizer pressure control system mainly controls the heater and spray valve by comparing the measured pressure of the pressurizer with the set value of

Main feed water temperature

Simulation calculation value Grey prediction value

Simulation calculation value Grey prediction value LSTM prediction value

LSTM model

Ht LSTM