燃煤电厂大数据挖掘和关键目标寻优智能系统研究Study on an Intelligent System of Big Data Mining and Key Target Optimization for Power Plant
虞仕杰,蒋赢凯,尹贵豪,翁浩斌
YU Shijie,JIANG Yinkai,YIN Guihao,WENG Haobin
摘要(Abstract)：
研究建立火电厂数据挖掘和关键目标寻优的智能系统，通过稳定工况划分、运行参数聚类、关键目标对标寻优等流程挖掘历史工况中有价值的信息，找到与当前实时运行工况近似的目标值及运行参数，可为优化运行提供开环建议。在锅炉效率关键目标的对标寻优实际工程应用中，系统发掘出在相近的MV（操作变量）、DV（扰动变量）参数情况下，实时工况炉效为92.13%而历史标杆工况炉效为93.34%，提出该工况下烟气氧量设定值由5.5%降低至4%仍可平稳运行，达到了运行提效、节约发电煤耗的目的。
This paper studies and establishes an intelligent system of data mining and key target optimization for thermal power plant. Through the process of stable operating condition division,operation parameter clustering and key target benchmarking optimization,valuable information in historical operating conditions is mined to find the target value and operation parameters similar to the current real-time operating conditions to provide open-loop suggestions for optimal operation. In the practical engineering application of benchmarking optimization of key targets of boiler efficiency,the system found that under similar MV(manipulation variable)and DV(disturbance variable)parameters,the boiler efficiency under real-time condition is 92.13% and that under historical benchmark condition is 93.34%. It is proposed that the setting value of oxygen in flue gas can be reduced from 5.5% to 4% under this condition. As a result,the boiler operation efficiency is improved and the coal consumption of power generation is saved.
关键词(KeyWords)：
燃煤电厂;大数据挖掘;运行优化;对标
coal-fired power plant;big data mining;operating optimization;benchmarking
基金项目(Foundation):
作者(Author):
虞仕杰,蒋赢凯,尹贵豪,翁浩斌
YU Shijie,JIANG Yinkai,YIN Guihao,WENG Haobin
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