Forecasting in-core power distributions in nuclear power plants via a spatial–temporal hierarchical-directed network

Abstract

As nuclear power plants (NPPs) undertake more peak regulation tasks to handle high new energy penetration and overcapacity, precise forecasting of in-core power distributions is essential for optimal control and safe operation. However, current works lack an effective strategy for predicting high-resolution power distributions and neglect in-core spatial correlations. This study proposes a spatial–temporal hierarchical-directed network (ST-HDN) for forecasting power distributions, whose prediction strategy is guided by the physical model. To characterize spatial correlations and causal relationships among physical quantities, the hierarchical-directed graph is designed and combined with neutron and power signals for input to the ST-HDN. Concretely, the ST-HDN integrates three sub-modules: a temporal-differencing layer to enhance representation of subtle variations; a multi-dilated convolutional network to extract dynamic temporal features; and a graph convolutional network to propagate spatial adjacent information, further predicting power nodes at various positions. The predicted power nodes are post-processed to derive future power distributions. Experiments on two peak regulation scenarios from a real-world NPP illustrate that the ST-HDN outperforms various state-of-the-art methods in 10-, 20-, and 30-min ahead forecasting.