摘要
针对压水堆核电站蒸汽发生器系统的强非线性特征,多参数耦合等因素导致的在线故障诊断算法稀缺的问题,提出一种基于自启发遗传算法的蒸汽发生器参数在线辨识方法。首先,基于参数辨识理论,构建模型驱动的自监督遗传算法框架,将故障诊断问题转化为系统关键性能参数的辨识问题。通过结合精细的系统机理模型,利用遗传算法将参数辨识任务重构为函数优化问题,从而有效克服非线性系统以及系统方程高阶微分项的求解限制。之后,构建基于动态时间规整适应度设计遗传算法的参数辨识方法,使用拟牛顿梯度下降思想优化遗传算法种群迭代策略,将全局随机搜索策略替换为沿梯度方向的定向搜索策略,解决了传统遗传算法收敛速度慢,难以满足在线系统参数辨识需求的问题。最终,基于模型数据与真实系统仿真机数据对提出的参数辨识方法进行性能验证,相较传统遗传算法降低了约5%的参数辨识误差,并平均减少了47%的算法收敛步数,证明了基于自启发遗传算法的参数辨识方法的有效性。
To address the scarcity of online fault diagnosis algorithms caused by strong nonlinear characteristics and multi-parameter coupling in pressurized water reactor(PWR)nuclear power plant steam generator systems,this paper proposes an online parameter identification method based on a self-inspired genetic algorithm(GA).First,a model-driven self-supervised GA framework is constructed based on parameter identification theory,transforming the fault diagnosis problem into the identification of key system performance parameters.By integrating a high-fidelity system mechanism model,the parameter identification task is reformulated as a function optimization problem,effectively overcoming the limitations imposed by nonlinearities and high-order differential terms in the system equations.Subsequently,a parameter identification method is developed by designing a fitness function based on dynamic time warping and optimizing the GA population iteration strategy using a quasi-Newton gradient descent approach.This replaces the global random search strategy with a gradient-directed search strategy,resolving the slow convergence issue of traditional GAs and meeting the requirements for online parameter identification.Finally,the proposed method is validated using both model data and real system simulator data.Compared to conventional GAs,it reduces parameter identification error by approximately 5%and decreases the average number of convergence steps by 47%,demonstrating the effectiveness of the self-inspired GA-based parameter identification method.
作者
郝峰霄
周金浛
高占宝
于劲松
唐荻音
闫蓓
Hao Fengxiao;Zhou Jinhan;Gao Zhanbao;Yu Jinsong;Tang Diyin;Yan Bei(Beihang University,Beijing 100191,China)
出处
《电子测量与仪器学报》
北大核心
2025年第10期70-78,共9页
Journal of Electronic Measurement and Instrumentation
基金
国家重点研发计划项目(2022YFB3304600)资助。
关键词
压水堆核电站
改进遗传算法
参数辨识
故障诊断
pressurized water reactor nuclear power plant
improved genetic algorithm
parameter identification
fault diagnosis
