Noise resistant steam generator water level reconstruction for nuclear power plant based on deep residual shrinkage network

Abstract

Nuclear power plant (NPP) steam generator (SG) water level has strong nonlinear pattern and lagging characteristic under control system and manual adjustment. SG water level signal accidental missing and frequent abnormity may lead to incorrect operational intervention or even emergency shutdown. Deep learning SG water level signal reconstruction method trained with historical sensor data can provide supportive information for operators’ decision-making to decrease the financial cost of maintenance and improve the safety of NPP assets. However, the sensor data transferred by electronic devices can be mixed up with noise in transients or complex conditions. This study proposed a noise resistant SG water level signal reconstruction method based on deep residual shrinkage network (DRSN). The dataset in this study is collected from a power-down process collected from a NPP in China. Results from the experiments under different levels of noise are conducted to illustrate the efficacy and robustness of the proposed approaches compared with other deep learning signal reconstruction methods.