基于SAC强化学习的核电事故诊断规程优化  

Optimization of Nuclear Power Accident Diagnosis Procedures Based on SAC Reinforcement Learning

作　　者：张大志 王志会 周华兵[2,3] 付永杰 习家轩 Zhang Dazhi;Wang Zhihui;Zhou Huabing;Fu Yongjie;Xi Jiaxuan(CNNC Key Laboratory on Nuclear Industry Simulation,China Nuclear Power Operation Technology Corporation,Ltd.,Wuhan,430040,China;College of Computer Science and Engineering,Wuhan Institute of Technology,Wuhan,430205,China;Hubei Provincial Key Laboratory of Intelligent Robot,Wuhan Institute of Technology,Wuhan,430205,China)

机构地区：[1]中核武汉核电运行技术股份有限公司,中核核工业仿真技术重点实验室,武汉430040 [2]武汉工程大学,计算机科学与工程学院,人工智能学院,武汉430205 [3]武汉工程大学,智能机器人湖北省重点实验室,武汉430205

出　　处：《核动力工程》2024年第S01期85-90,共6页Nuclear Power Engineering

摘　　要：基于Soft Actor-Critic (SAC)算法的核电事故诊断规程优化方法,以决策树模型为基础,对事故检测规程判断策略进行优化,在显著提高事故检测性能的同时保持了决策模型的可解释性。模型使用SAC作为强化学习算法,将状态定义为当前运行数据和历史数据的组合,动作设定为诊断规程决策阈值的调整,回报反映了诊断的准确性。借助SAC算法,系统不断地调整阈值进行策略优化以获得最佳的诊断效果。在主蒸汽管道破裂(MSLB)模拟工况事故中,模型能更好地适应和理解复杂高维数据,找到特定性能指标下的最优控制策略,准确率稳步趋近于1。本文方法显著减少了误判率,不仅更准确地检测核电事故,而且在减少误警方面表现出优秀的结果,提高了核电运行的安全性。This paper proposes an optimization method for nuclear accident diagnosis procedures based on the Soft Actor-Critic(SAC)reinforcement learning model.Using a decision tree model as the foundation to optimize the judgment strategy of accident detection procedures,which significantly improves the performance of accident detection while maintaining the interpretability of the decision model.The model employs SAC as the reinforcement learning algorithm,which defines the state as a combination of current operating data and historical data,sets the actions as the adjustment of the decision threshold of diagnostic procedures,and reflects the accuracy of diagnosis through the returns.With the help of SAC algorithm,the system constantly adjusts the threshold to optimize the strategy to obtain the best diagnosis effect.In a simulated Main Steam Line Break(MSLB)accident scenario,the model can better adapt to and comprehend complex high-dimensional data,find the optimal control strategy under specific performance indicators,and the accuracy is steadily approaching 1.The proposed method significantly reduces the false positive rate,and it not only detects nuclear power accidents more accurately,but also shows excellent results in reducing false alarms,thus improving the safety of nuclear power operation.

关 键 词：核电事故 强化学习 规程优化 MSLB 

分 类 号：TL334[核科学技术—核技术及应用]