摘要
发电厂高效运行要求对凝汽器模型进行精确优化,特别是在预测真空度方面——这是性能评估的关键指标. 传统方法要么采用具有高精度但可能存在解释问题的数据驱动模型,要么采用基于物理原理但面临预测准确性挑战的机理模型. 因此,提出了一种创新的凝汽器真空度机理模型参数优化方法,以实现在预测准确性和理论解释之间取得平衡。具体而言,我们的方法首先建立了以传热学原理为基础的机理模型,然后利用精英策略增强的遗传算法进行参数优化,最终得到能够准确预测凝汽器真空度等各项输出的凝汽器机理模型。通过在火电厂收集得到的真实数据上的实验分析验证了所提方法的有效性,展示了其在准确预测凝汽器真空度和提高火电厂运行效率方面的潜力。
Efficient power plant operation demands precise optimization of condenser models,particularly in predicting vacuum degree—a critical metric for performance assessment.Traditional approaches employ either data-driven models with adaptability but potential interpretability issues,or mechanism models grounded in physical principles but facing predictive accuracy challenges.In this paper,we propose an innovative approach to strike a balance between empirical accuracy and theoretical understanding for enhanced condenser vacuum degree prediction in thermal power plant operations.Specifically,our method involves initiating with a mechanism model grounded in heat transfer principles and employing an Elitism Strategy-enhanced genetic algorithm for parameter optimization.Experimental analysis on real-world data validates the effectiveness of the proposed method,showcasing its potential for accurately predicting condenser vacuum degree and enhancing operational efficiency in thermal power plants.
作者
陈刚
乔依林
刘星辰
倪向红
CHEN Gang;QIAO Yilin;LIU Xingchen;NI Xianghong(Guoneng Nanjing Electric Power Test and Research Co.,Ltd.,Nanjing 210000,China)
出处
《自动化与仪器仪表》
2025年第10期63-67,共5页
Automation & Instrumentation
基金
国家能源集团科技项目GJNY-23-70。
关键词
机理模型
凝汽器
真空度
遗传算法
精英策略
mechanism model
condenser
vacuum degree
genetic algorithm
elitism strategy
