A multivariate operational situation awareness method based on weighted graph structure for complex nuclear power systems

Operational situation awareness is a key technology to ensure the operation safety of complex nuclear power systems. The existing parallel monitoring method cannot sense latent abnormalities and the trend of abnormal evolution. Simultaneously, the monitoring data of nuclear power systems are characterized by multi-source, high-dimensional, strong coupling, etc. The operational situation of a single variable is affected by other variables to varying degrees, and its correlation cannot be quantitatively evaluated. This paper proposes a multivariate operational situation awareness method for complex nuclear power systems based on a weighted graph structure. First, WGS is used to learn to characterize the dependencies and weights among complex multivariate data and construct an interpretable graph structure. Second, attention-based LSTM is used to realize the measurement point of operational situation awareness. Ultimate, the method is validated on the operational monitoring data of the main and auxiliary systems of a nuclear power unit in one circuit. Compared with ATT-LSTM, WGS-LSTM's computational accuracy and speed are improved by 71.43% and 11.78%, respectively, which can effectively sense the operational situation of complex nuclear power systems.