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电站锅炉燃烧异常原因诊断方法应用 被引量:2

Diagnosis Method for the Abnormal Combustion in Power Station Boilers
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摘要 对某620 MW超临界参数、配置双进双出制粉系统机组的运行数据进行挖掘,获得热一次风压、容量风门开度及磨煤机料位对火焰稳定性变化的敏感值域,推荐了用于运行参数异常诊断的判定依据。结果表明:热一次风压的敏感值域为0~8.3 kPa,稳定门槛值、突变判定阈值、连续变化判定阈值和异常波动判定阈值分别为6.8 kPa、200 Pa、80 Pa和80 Pa;容量风门开度的敏感值域为0~40%,稳定门槛值、突变判定阈值、连续变化判定阈值和异常波动判定阈值分别为26%、10%、4%和4%;磨煤机料位的敏感值域为0~400 Pa,稳定门槛值、突变判定阈值、连续变化判定阈值和异常波动判定阈值分别为300、150、60和60 Pa;根据所述诊断方法及相关定值对该机组火焰异常工况的原因进行实时分析时,诊断结论正确。 Based on the operation data analysis of a 620 MW super-critical unit assembled with double-in and double-out mills,the sensitive range of some operating parameters to the variation of flame stability is obtained,including hot primary air pressure,open position of capacity valve and coal level of a mill.The criteria for diagnosing the abnormal variation of the operating parameters are recommended.The sensitive range of the hot primary air pressure is 0~8.3 kPa,while the stable threshold,sudden change threshold,successive change threshold and abnormal fluctuation threshold are 6.8 kPa,200 Pa,80 Pa and 80 Pa,respectively.As to the open position of capacity valve,sensitive range,stable threshold,sudden change threshold,successive change threshold and abnormal fluctuation threshold are 0~40%,26%,10%,4% and 4%,respectively,and the corresponding values for the open position of coal level of a mill are 0~400 Pa,300 Pa,150 Pa,60 Pa and 60 Pa,respectively.By using the method and recommended values proposed in this work to conduct the cause analysis for the flame abnormality,the diagnostic conclusion is correct.
作者 王锡辉 陈厚涛 朱晓星 肖刚 WANG Xi-hui;CHEN Hou-tao;ZHU Xiao-xing;XIAO Gang(State Grid Hu Nan Electric Power Company Research Institute,Hunan Xiangdian Test and Research Institute Co.Ltd.,Changsha,China,410007;Institute for Thermal Power Engineering,Zhejiang University,Hangzhou,China,310027)
出处 《热能动力工程》 CAS CSCD 北大核心 2020年第3期256-262,共7页 Journal of Engineering for Thermal Energy and Power
基金 国家自然科学基金(51776168) 湖南省科技创新平台与人才计划(2016TP1027)。
关键词 燃烧诊断 电站锅炉 智能电厂 在线分析 工程应用 combustion diagnosis power station smart power plant online analysis engineering application
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