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稳态检测是电站锅炉建模、稳态优化中必不可少的重要环节,本研究采用机组功率、主蒸汽压力、总给煤量、总风量、水煤比和中间点温度这6个重要的运行变量对电站锅炉稳态进行检测。先利用变量的变化速度和加速度数据序列分别计算其稳态指数,再进行多变量加权获得综合稳态指数用于电站锅炉稳态判断。为了确保稳态检测算法对变量剧烈变化的响应速度,提出了多变量权值自适应修正算法,综合利用各变量的趋势分量和瞬态分量对权值进行自适应修正。以1 000 MW机组运行历史数据对算法进行验证表明:提出的多变量权值自适应修正算法能较准确判断锅炉稳态,满足锅炉燃烧建模及稳态优化需求。
Steady-state detection is an essential part of power plant boiler modeling and steady-state optimization. Six important factors such as unit power, main steam pressure, total coal supply, total air volume, water-coal ratio and midpoint temperature Operational Variables Detect Station Steady State of Power Station. Firstly, the steady index of the variable speed and the acceleration data sequence are respectively calculated, and then the multivariable weight is used to obtain the comprehensive steady index for the steady state judgment of the power station boiler. In order to ensure the response speed of the steady-state detection algorithm to the drastic changes of the variables, a multivariable adaptive weight correction algorithm is proposed. The trend components and the transient components of each variable are used to adaptively correct the weights. The verification of the algorithm with 1 000 MW unit operation history data shows that the proposed multivariable weight adaptive correction algorithm can accurately determine the boiler steady state and meet the boiler combustion modeling and steady-state optimization requirements.