期刊文献+

基于支持向量机的水轮机特性建模 被引量:1

Modeling of Hydraulic Turbine Characteristic Based on Support Vector Machine
在线阅读 下载PDF
导出
摘要 水轮机运行中要考虑空化问题和稳定性问题,因此,有必要建立水轮机空化特性和压力脉动特性的数学模型。针对经过试验得到的水轮机空化特性和压力脉动特性相对应的空间曲面比较复杂的特点,本文研究用支持向量机对水轮机空化特性和压力脉动特性进行建模,并应用于四川紫坪铺水力发电厂中的水轮机空化特性和压力脉动特性的建模中。建模结果表明,水轮机空化特性和压力脉动特性的支持向量机双输出模型,能真实的表达水轮机的空化特性和压力脉动特性,并具有较强的泛化能力,可以在控制水轮发电机组的运行等方面得到应用。 The problems of cavitation and stability need to be taken into account during the hydraulic turbine operation,so it is necessary to establish the mathematical model which can express the cavitation and pressure fluctuation characteristics of hydraulic turbine.As the corresponding curve of the cavitation and pressure fluctuation characteristics,which was obtained from experiments,was too complex to be expressed by expressions.In this paper,a new modeling method using support vector machine was given,and was used in modeling the cavitation and pressure fluctuation characteristics of the turbine in Zipingpu Hydropower Station,Sichuan Province.Results show that the two outputs model which is based on support vector machine is in good agreement with the test results of the cavitation and pressure fluctuation characteristic,and it is good in generalization.So it can be applied to the operation controlling of generating sets.
出处 《现代机械》 2010年第4期22-24,共3页 Modern Machinery
关键词 支持向量机 水轮机 空化特性 压力脉动特性 support vector machine hydraulic turbine cavitation characteristics pressure fluctuation characteristics
  • 相关文献

参考文献12

  • 1晏敏 张昌期.水轮机特性的超曲面正交多项式最小二乘拟合[J].大电机技术,1986,(4):60-63.
  • 2程远楚.水轮机瞬变过程计算的改进抛物线插值法[J].水电站设计,1997,13(1):57-61. 被引量:5
  • 3徐枋同 李永华.系统辨识理论与实践[M].北京:中国电力出版社,1999..
  • 4Vapnic V N.统计学习理论[M].北京:电子工业出版社,2004:365-373.
  • 5Haykin S Neural Networks a Comprehensive Foundation[M].New Jersey,Prentice-Hall 1999:318-347.
  • 6Thissen U,Van Brakel R,De Weijer A P,et al.Using Support Vector Machines for Time Series Prediction[J].Chemometrics and Intelligent Laboratory Systems,2003,69(1):35-49.
  • 7Tay F E H,Cao L J.Modified Support Vector Machines in Financial Time Series Forcasting[J].Neurocomputing 2002,48(1-4):847-861.
  • 8Thissen U,Pepers M,Ustun B,et al.Comparing Support Vector Machine to PLS for Special Regression Applications[J].Chemometrics and Intelligent Laboratory Systems,2004,73(2):169-179.
  • 9赵林明,王海燕,何睿,田新志.水轮机空化特性的人工神经网络模型[J].水利学报,2006,37(7):893-897. 被引量:4
  • 10卢建奎,王亚林.紫坪铺电站水轮机特性及运行可靠性分析[J].四川水力发电,2004,23(2):19-21. 被引量:9

二级参考文献11

共引文献18

同被引文献17

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部