哈尔滨工程大学学报2023，Vol.44Issue(12)：2128-2134,7.DOI:10.11990/jheu.202309018
混合神经网络的核电站故障程度评估方法
Hybrid neural network for evaluating the fault degree of nuclear power plants
摘要
Abstract
To accurately process a large amount of complex data and avoid operator judgment errors caused by in-creased operator pressure,this paper proposed a hybrid neural network hyperparameter optimization method based on an improved particle swarm optimization algorithm and leave-one-out cross validation to assist operators in evalu-ating the degree of failure in nuclear power plants.This method optimized hyperparameter combinations through an improved particle swarm optimization algorithm,evaluated the generalization performance of deep learning models through leave-one-out cross validation,and ultimately constructed a high-precision failure severity evaluation mod-el.The proposed method was tested and evaluated by considering the loss of coolant accident condition.The results show that the proposed method for evaluating the degree of nuclear power plant failure based on hybrid neural net-work hyperparameter optimization can search for the optimal combination of hyperparameters,achieving an absolute accuracy of 97%in neural network modeling and effectively evaluating the failure degree of the nuclear power plant to assist the operator in maintenance decisions.关键词
核电站/故障程度评估/超参数优化/混合神经网络/粒子群算法/留一交叉验证/破口事故/操纵员决策Key words
nuclear power plant/fault degree evaluation/hyperparameter optimization/hybrid neural network/par-ticle swarm optimization/leave-one-out cross validation/loss of coolant accident/operator decision分类
能源科技引用本文复制引用
周桂,王航,彭敏俊..混合神经网络的核电站故障程度评估方法[J].哈尔滨工程大学学报,2023,44(12):2128-2134,7.基金项目
工业和信息化部核能开发项目(KY11500200118). （KY11500200118）
