Clean Coal Technology

2022, v.28;No.142(06) 199-205

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基于机器学习的循环流化床机组出力预测
Performance prediction of circulating fluidized bed unit based on machine learning

韩义;张奇月;段伦博;王研凯;于英利;付旭晨;荣俊;孙世超;
HAN Yi;ZHANG Qiyue;DUAN Lunbo;WANG Yankai;YU Yingli;FU Xuchen;RONG Jun;SUN Shichao;Inner Mongolia Electric Power Research Institute Branch,Inner Monglia Electric Power (Group)Co.,Ltd.;Key Laboratory of Energy Thermal Conversion and Control,Ministry of Education,Southeast University;

摘要(Abstract):

煤电是电力系统清洁低碳转型中重要的支撑性和调节性电源,然而劣质煤掺烧等因素造成的煤电机组技术出力受阻,严重影响电网安全运行与新能源电力消纳。鉴于此,提出一种基于机理仿真与数据驱动深度融合的映射模型建立方法,利用机理仿真构造锅炉热力系统样本空间,基于数理映射进行机组出力预测。兼顾机理仿真的理论准确性和数理映射的强泛化深入性,实现多因素耦合条件下的循环流化床机组动态边界出力预测及出力受阻因素分析。经300 MWe级示范机组测试表明,考虑辅机受限、受热面参数超限、关键参数超限3方面影响因素,分别设置给煤机、引风机、冷渣器、床温、屏过壁温、流化风速等运行参数超限报警值,机理仿真最大偏差为3℃,误差率小于2%。基于主成分分析法筛选设计输入为7、输出为1的3层架构BP神经网络模型,经遗传算法完成网络优化后,采用32个训练样本与5个测试样本,进行网络训练与预测。模型训练相对误差在±1.2%内,模型预测相对误差在±1.5%内,具有较高的精确度和泛化能力。
Coal power is an important supporting and regulating power supply in the clean and low-carbon transformation of power system. However, the technical output of thermal power units is hindered due to factors such as low-quality coal combustion, which seriously affects the safe operation of power grid and new energy power consumption. In view of this, a projection model building method based on the integration of mechanism simulation and data drive was presented in this paper. The sample space of boiler thermal system was constructed by mechanism simulation, and the unit output prediction was carried out based on mathematical projection. Considering the theoretical accuracy of mechanism simulation and the strong generalization of mathematical projection, the dynamic boundary output prediction of circulating fluidized bed units and the analysis of output blocking factors were realized under the condition of multi-factor coupling. The test results of 300 MWe demonstration unit shows that: considering the three influencing factors of auxiliary machine limitation, heating surface parameter overrun and key parameter overrun, the alarm values for exceeding the limit of operating parameters such as coal feeder, induced draft fan, slag cooler, bed temperature, screen wall temperature and fluidization wind speed are set respectively. The maximum deviation of mechanism simulation is 3 ℃,and the error rate is less than 2%. The BP neural network model with 7 inputs and 1 outputs is screened and designed based on the principal component analysis method. After network optimization by genetic algorithm, the network training and prediction are carried out by using 32 training samples and 5 test samples. The relative error of model training is within ±1.2%,the relative error of model prediction is within ±1.5%,ind icating that there is high accuracy, generalization ability, and worth reference.

关键词(KeyWords): 数据驱动;出力预测;循环流化床;数理模型;主成分分析法;遗传算法
data drive;output prediction;circulating fluidized bed boiler;mathematical model;principal component analysis method;genetic algorithm

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划资助项目(2019YFE0100100-08)

作者(Authors): 韩义;张奇月;段伦博;王研凯;于英利;付旭晨;荣俊;孙世超;
HAN Yi;ZHANG Qiyue;DUAN Lunbo;WANG Yankai;YU Yingli;FU Xuchen;RONG Jun;SUN Shichao;Inner Mongolia Electric Power Research Institute Branch,Inner Monglia Electric Power (Group)Co.,Ltd.;Key Laboratory of Energy Thermal Conversion and Control,Ministry of Education,Southeast University;

DOI: 10.13226/j.issn.1006-6772.21061701

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