汽轮机变工况下调节级压力预测模型及应用
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摘要
为监视汽轮机通流部分的健康状况和进行故障早期诊断,以汽轮机变工况计算为理论基础,基于弗留格尔公式和Weierstrass逼近定理,建立了调节级压力的多元回归模型。根据某660MW间接空冷机组大修后的运行数据对模型进行回归分析和验证,结果表明:模型具有很高的拟合度,自变量对因变量影响显著;稳态工况时,预测结果与实测值趋势一致,相对误差约为1.2%,实现了变工况下调节级压力软测量。将模型应用于电厂分布式控制系统中,建立了汽轮机通流部分故障的预警系统。
Using the calculation of the variable conditions for steam turbines as the theoretical basis, a multivariate regression model for regulating pressure was established based on the Frugell formula and the Weierstrass approximation theorem. According to the historical operation data of a 660 MW generating set, the model was subjected to regression analysis and verification. The results show that the model has a high degree of fit, and the independent variable has a significant influence on the dependent variable; When the steady state conditions are met, the predicted results are consistent with the measured values and the relative error is about 1.2%, which enable soft pressure measurement of the regulation stage. The model was applied to the distributed control system(DCS)of the power plant, and an on-line warning system was established to adjust the pressure anomaly and the partial flow malfunction in the turbine under variable conditions.
Using the calculation of the variable conditions for steam turbines as the theoretical basis, a multivariate regression model for regulating pressure was established based on the Frugell formula and the Weierstrass approximation theorem. According to the historical operation data of a 660 MW generating set, the model was subjected to regression analysis and verification. The results show that the model has a high degree of fit, and the independent variable has a significant influence on the dependent variable; When the steady state conditions are met, the predicted results are consistent with the measured values and the relative error is about 1.2%, which enable soft pressure measurement of the regulation stage. The model was applied to the distributed control system(DCS)of the power plant, and an on-line warning system was established to adjust the pressure anomaly and the partial flow malfunction in the turbine under variable conditions.
引文
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[15]杨勇平,袁星,黄圣伟,等.火电机组湿法脱硫系统能耗的回归分析[J].工程热物理学报,2012,33(11):54-56.
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[2]杨涛,胥建群,周克毅,等.汽轮机通流部分结垢与热力参数变化的关系分析[J].江苏电机工程,2013,32(2):71-74.
[3]刘志江,李续军,刘向民.超临界压力汽轮机固体颗粒侵蚀的表面硬化处理技术[J].中国电力,1996,37(2):25-28.
[4]朱永利,尹金良.人工智能在电力系统中的应用研究与实践综述[J].发电技术,2018,39(2):106-111.
[5]王建国,孟娜,殷鑫.基于粒子群优化算法的凝汽器真空预测模型[J].动力工程学报,2012,32(10):815-820.
[6]邓博,徐鸿,郭鹏,等.变负荷下超临界机组过热器壁温预测[J].中国电力,2018,51(3):13-20.
[7]孙宝民,信晶,杨斌,等.基于独立主成分预测电站锅炉NOx[J].中国电力,2018,51(3):39-43.
[8]李维特,黄保海.汽轮机变工况热力计算[M].北京:中国电力出版社,2001:143-145.
[9]靳智平.电厂汽轮机原理及系统[M].北京:中国电力出版社,2004:88-92.
[10]将尔雄,赵风光.数值逼近[M].上海:复旦大学出版社,1996:183-185.
[11]李慧君,赵翔.汽轮机调节级特性曲线快速算法改进[J].热力发电,2016,45(10):94-98.
[12]张利平,王夕阳,王睿,等.基于MATLAB的汽轮机调节级变工况计算[J].华北水利水电大学学报,2015,36(3):72-72.
[13]颜节礼,唐建荣.应用统计学[M].西安:西安电子科技大学出版社,2016:103-104.
[14]Jiang X,Liu P,Li Z.Data reconciliation and gross error detection for operational data in power plants[J].Energy,2014(75):14-23.
[15]杨勇平,袁星,黄圣伟,等.火电机组湿法脱硫系统能耗的回归分析[J].工程热物理学报,2012,33(11):54-56.
[16]杨勇平,杨昆.汽轮机通流部分故障的热力判据[J].热能动力工程,1999,14(5):347-349.
[17]李勇,徐君诏,黄萍力.凝汽式汽轮机通流部分结垢诊断方法研究[J].汽轮机技术,2008,50(5):286-287.