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.
作者
李晓恩
王宁玲
张雨檬
杨志平
杨勇平
LI Xiaoen, WANG Ningling, ZHANG Yumeng, YANG Zhiping, YANG Yongping(National Thermal Power Engineering ＆ Technology Research Center, North China Electric Power University, Changping District, Beijing 102206, Chin)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2018年第5期1518-1524,共7页
Proceedings of the CSEE
基金
国家重点基础研究发展计划项目(973计划)(2015CB251505)
中央高校基本科研业务费专项资金项目(2015MS43,2015XS81,2014XS19,2014XS13)~~
关键词
直接空冷系统
背压
属性重要度
加权支持向量回归
模糊粗糙集
direct air-cooling system
back pressure
attributes significant
weighted support vector regression
fuzzyrough sets
