Reactor Coolant Pump Leakage Estimation of PWR Based on Broad Learning System

Abstract

Loss-of-coolant accident (LOCA) of reactor coolant pump is considered as a critical issue in pressurized water reactor (PWR) accident analysis. Much work focus on the detection of LOCA, while little work has been done on leakage quantity estimation. In this paper, a reactor coolant leakage estimation model is proposed based on broad learning system (BLS) model which takes the advantages of the flatted structure and incremental learning. Considering that larger leakage is more concerned, the BLS model is improved by a weighted loss function. Dropout layer and white noise are added to improve the robustness of the model. This model is designed to estimate the coolant leakage in an online manner with high precision. The proposed model is evaluated on real leakage data of a reactor coolant pump. Experiments show that, with similar accuracy, the proposed model is significantly faster than the state-of-art deep neural networks.