Soft sensor modeling for the efficiency of steam turbine last stage group using Support Vector Machine Regression

摘要：

To calculate the steam turbine exhaust enthalpy, this paper proposes a soft sensor method by using the support vector machine regression (SVR). The proposed method is based on the following three-step strategy. Firstly, main factors, influencing on the last stage group efficiency, were discovered through mechanism analysis. Secondly, based on the designed sample data, the support vector machine regression is used to establish the functional relationship between the exhaust enthalpy and these main factors. To identify the parameters involved in the SVR, the genetic algorithm (GA) is taken as the optimizer. Finally, some experimental sample data collected from a 600MW unit are used to validate the established soft sensor model. The results show that the proposed method has high prediction accuracy, by comparing with thermal test data.

关键词： Exhaust Enthalpy Efficiency of the Last Stage Group Model Parameter Optimization

作者: Zhao Xiuya Wang Peihong Li Bing

作者单位: School of Energy and Environment, Southeast University, Nanjing, 210096, China Nanjing Nari-Relays Electric Co., Ltd

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 1113-1116

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）