Multi-Feature Fusion Multi-Step State Prediction of Nuclear Power Sensor Based on LSTM

Abstract

Aiming at the problem in the prediction of nuclear power working condition parameter, this paper uses a large number of time series collected by the nuclear power plant sensor detection system to propose a multi-feature fusion multi-step state prediction model based on long short-term memory network (LSTM). This paper takes the SG1 steam pressure sensor data collected by the real-time parameter system of nuclear power plants as the research object. Firstly, the data is preprocessed for the problems of missing data and inconsistent sampling time scales, and then the structural design and modeling of the multi-feature fusion multi-step state prediction model is completed based on LSTM. Finally, the prediction model proposed in this paper is compared with the multi-step prediction models such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Model-S1 layer and univariate LSTM. Experimental results show that the fitting performance and prediction performance of the prediction model proposed in this paper are with overall optimization, and it also verifies the applicability of the deep learning method based on the LSTM model in the field of nuclear power plant operation safety assurance.