考虑属性重要度的空冷系统背压特性模型
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摘要
与传统湿冷系统相比,直接空冷系统更易受到多种边界条件变化的影响。针对理论公式与实际应用的差异以及属性重要度差异,基于运行数据建立了考虑属性重要度的背压特性模型。根据机组连续1个月的实际运行数据结合模糊粗糙集理论对影响空冷运行的各条件属性进行客观加权,并通过加权支持向量回归方法,建立了直接空冷系统的背压模型。结果表明:选取主汽流量、主汽压力、环境温度、风机转速、环境风速、大气压力作为条件属性可得到精度较高的加权回归模型,7月份与10月份的数据分析下,回归模型精度均较高,均方根误差分别为0.70、0.76kPa。同时,结果表明,加权过程可在不影响模型精度的情况下,有效降低模型计算量,提高模型泛化能力。
The direct air-cooling condenser is more sensitive to the changing of various boundary conditions than the traditional water-cooling system. By considering the significance of each attributes, a back pressure model was built with the practical operating data. The historical data in one-month operation was used to calculate the weight of each condition attributes based on fuzzy rough sets(FRS). The weighted support vector regression(SVR) method was introduced to build the regression model. The result shows that the condition attributes, consisting of mass flowrate and pressure of main steam, ambient temperature, frequency of air fan, wind speed and atmospheric pressure, well reflect the back pressure with a RMSE of 0.70 kPa in July and 0.76 kPa in October; the weighting process would improve the generalization ability and reduce the computation consumption without decreasing the accuracy.
The direct air-cooling condenser is more sensitive to the changing of various boundary conditions than the traditional water-cooling system. By considering the significance of each attributes, a back pressure model was built with the practical operating data. The historical data in one-month operation was used to calculate the weight of each condition attributes based on fuzzy rough sets(FRS). The weighted support vector regression(SVR) method was introduced to build the regression model. The result shows that the condition attributes, consisting of mass flowrate and pressure of main steam, ambient temperature, frequency of air fan, wind speed and atmospheric pressure, well reflect the back pressure with a RMSE of 0.70 kPa in July and 0.76 kPa in October; the weighting process would improve the generalization ability and reduce the computation consumption without decreasing the accuracy.
引文
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[20]Guo Sisi,Liu Pei,Li Zheng.Estimation of exhaust steam enthalpy and steam wetness fraction for steam turbines based on data reconciliation with characteristic constraints[J].Computers&Chemical Engineering,2016,93:25-35.
[21]郭民臣,谢静岚,李鹏.变工况下直接空冷机组最佳真空的分析[J].动力工程学报,2012,32(7):542-546.Guo Minchen,Xie Jinglan,Li Peng.Analysis on optimum vacuum of direct air-cooled unit under variable working conditions[J].Journal of Chinese Society of Power Engineering,2012,32(7):542-546(in Chinese).
[22]杨立军,杜小泽,杨勇平.空冷凝汽器全工况运行特性分析[J].中国电机工程学报,2008,28(8):24-28.Yang Lijun,Du Xiaoze,Yang Yongping.Performance analysis of air-cooled condensers at all operating conditions[J].Proceedings of the CSEE,2008,28(8):24-28(in Chinese).
[23]张兆营,杨建国,郑立军.1000 MW直接空冷发电机组凝汽器传热性能试验研究[J].中国电机工程学报,2014,34(20):3317-3323.Zhang Zhaoying,Yang Jianguo,Zheng Lijun.Experimental research on heat transfer performance of condenser used in 1000 MW direct air-cooled generator unit[J].Proceedings of the CSEE,2014,34(20):3317-3323(in Chinese).
[2]冯澎湃,王宁玲,杨志平,等.直接空冷高背压供热机组的梯级供热特性与冷端变工况协同优化[J].中国电机工程学报,2016,36(20):5546-5554.Feng Pengpai,Wang Ningling,Yang Zhiping,et al.Cascade heating characteristics and off-design collaborative optimization of direct air-cooled high pressure heat supply power units[J].Proceedings of the CSEE,2016,36(20):5546-5554(in Chinese).
[3]Chen Lei,Yang Lijun,Du Xiaoze,et al.A novel layout of air-cooled condensers to improve thermo-flow performances[J].Applied Energy,2016,165:244-259.
[4]刘丽华,杜小泽,席新铭,等.直接空冷火电机组风机群分区调节运行实验[J].中国电机工程学报,2013,33(17):71-77.Liu Lihua,Du Xiaoze,Xi Xinming,et al.Experiments on divisional regulation of fan cluster for direct air-cooled power generating units[J].Proceedings of the CSEE,2013,33(17):71-77(in Chinese).
[5]杨勇平,吴殿法,王宁玲,等.一种考虑权重不确定性的机组综合评价模型[J].华北电力大学学报:自然科学版,2016,43(2):73-79.Yang Yongping,Wu Dianfa,Wang Ningling,et al.Comprehensive performance evaluation model for power units considering weight uncertainty[J].Journal of North China Electric Power University,2016,43(2):73-79(in Chinese).
[6]李勇,何蕾,庞传军,等.基于层次分析法的火电厂运行情况量化评价方法[J].电网技术,2015,39(2):500-504.Li Yong,He Lei,Pang Chuanjun,et al.An analytical hierarchy process based quantitative method to evaluate operating condition of thermal power plant[J].Power System Technology,2015,39(2):500-504(in Chinese).
[7]Liu Lihua,Du Xiaoze,Xi Xinming.Experimental analysis of parameter influences on the performances of direct air cooled power generating unit[J].Energy,2013,56:117-123.
[8]刘兴杰,岑添云,郑文书,等.基于模糊粗糙集与改进聚类的神经网络风速预测[J].中国电机工程学报,2014,34(19):3162-3169.Liu Xingjie,Cen Tianyun,Zheng Wenshu,et al.Neural network wind speed prediction based on fuzzy rough set and improved clustering[J].Proceedings of the CSEE,2014,34(19):3162-3169(in Chinese).
[9]Pawlak Z.Rough sets[J].International Journal of Computer&Information Sciences,1982,11(5):341-356.
[10]Yang Yanyan,Chen Degang,Wang Hui,et al.Fuzzy rough set based incremental attribute reduction from dynamic data with sample arriving[J].Fuzzy Sets and Systems,2017,312:66-86.
[11]付鹏,王宁玲,李晓恩,等.基于信息物理融合的火电机组节能环保负荷优化分配[J].中国电机工程学报,2015,35(14):3685-3692.Fu Peng,Wang Ningling,Li Xiao’en,et al.CPS-based load dispatching model for the energy conversation and emission reduction of thermal power units[J].Proceedings of the CSEE,2015,35(14):3685-3692(in Chinese).
[12]Du Xiaoze,Liu Lihua,Xi Xinming,et al.Back pressure prediction of the direct air cooled power generating unit using the artificial neural network model[J].Applied Thermal Engineering,2011,31(14-15):3009-3014.
[13]Costa D C L,Nunes M V A,Vieira J P A,et al.Decision tree-based security dispatch application in integrated electric power and natural-gas networks[J].Electric Power Systems Research,2016,141:442-449.
[14]齐敏芳,付忠广,陈菲,等.火电机组主蒸汽流量在线监测计算方法[J].华北电力大学学报,2015,42(1):51-57.Qi Minfang,Fu Zhongguang,Chen fei,et al.Research on on-line monitoring calculation method of main steam flow in thermal power units[J].Journal of North China Electric Power University,2015,42(1):51-57(in Chinese).
[15]Chen Degang,He Qiang,Wang Xizhao.FRSVMs:Fuzzy rough set based support vector machines[J].Fuzzy Sets and Systems,2010,161(4):596-607.
[16]Vapnik V N.Statistical learning theory[M].New York:Wiley,1998.
[17]Yoo K H,Back J H,Na M G,et al.Prediction of golden time using SVR for recovering SIS under severe accidents[J].Annals of Nuclear Energy,2016,94:102-108.
[18]Chang C C,Lin C J.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology(TIST),2011,2(3):27.
[19]Jiang Xiaolong,Liu Pei,Li Zheng.A data reconciliation based framework for integrated sensor and equipment performance monitoring in power plants[J].Applied Energy,2014,134:270-282.
[20]Guo Sisi,Liu Pei,Li Zheng.Estimation of exhaust steam enthalpy and steam wetness fraction for steam turbines based on data reconciliation with characteristic constraints[J].Computers&Chemical Engineering,2016,93:25-35.
[21]郭民臣,谢静岚,李鹏.变工况下直接空冷机组最佳真空的分析[J].动力工程学报,2012,32(7):542-546.Guo Minchen,Xie Jinglan,Li Peng.Analysis on optimum vacuum of direct air-cooled unit under variable working conditions[J].Journal of Chinese Society of Power Engineering,2012,32(7):542-546(in Chinese).
[22]杨立军,杜小泽,杨勇平.空冷凝汽器全工况运行特性分析[J].中国电机工程学报,2008,28(8):24-28.Yang Lijun,Du Xiaoze,Yang Yongping.Performance analysis of air-cooled condensers at all operating conditions[J].Proceedings of the CSEE,2008,28(8):24-28(in Chinese).
[23]张兆营,杨建国,郑立军.1000 MW直接空冷发电机组凝汽器传热性能试验研究[J].中国电机工程学报,2014,34(20):3317-3323.Zhang Zhaoying,Yang Jianguo,Zheng Lijun.Experimental research on heat transfer performance of condenser used in 1000 MW direct air-cooled generator unit[J].Proceedings of the CSEE,2014,34(20):3317-3323(in Chinese).