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.

中文翻译：

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基于LSTM神经网络的核电系统仿真与状态预测研究。

核电厂系统相互耦合，其运行条件是可变的且复杂的。在这些系统中出现操作故障的情况下，将会有大量的警报参数，这可能在巨大压力下导致人身伤害。因此，有必要预测设备系统关键参数的值。关键参数值的预测可以帮助运营商提前确定系统参数的变化趋势，从而可以有效地提高系统安全性。在本文中，开发了深度学习长短期记忆（LSTM）神经网络模型来预测核电厂的关键参数。仿真结果验证了所提出的网络，并与传统的灰色理论进行了比较。