基于PSO优化LS-SVM算法的水电站厂房结构振动响应预测  

Prediction of vibration response of powerhouse structures based on LS-SVM optimized by PSO

作　　者：练继建[1] 何龙军[1] 王海军[1] 

机构地区：[1]天津大学水利工程仿真与安全国家重点实验室,天津300072

出　　处：《中国工程科学》2011年第12期45-50,共6页Strategic Study of CAE

基　　金：国家杰出青年基金(50725929);国家自然科学基金创新研究群体科学基金(51021004);教育部"长江学者和创新团队发展计划"创新团队(IRT0851);国家基金青年基金项目(50909072);教育部新教师基金(20090032120082)

摘　　要：依据二滩水电站地下厂房和机组的原型观测数据对机组和厂房结构振动的相关性进行分析,据此建立基于粒子群优化最小二乘支持向量计算法的厂房振动响应预测模型,预测结果与实测资料吻合。在此基础上将运行水头作为输入因子引入到智能预测模型中,扩大了该智能预测模型的适用范围,取得了很好的效果。The vibration of powerhouse structures is mainly induced by hydraulics factors, mechanical and electromagnetic factors of the generating unit. It nonlinearly couples with the generating unit. Based on prototype observation data of Ertan Hydropower Station, the paper analyzes the coupling effect between vibration of units and powerhouse, and then the vibration response forecasting model of the powerhouse is built based on LS-SVM opti- mized by particle swarm optimization algorithm, and the prediction results are coincide with the observed data. Fur- ther, the paper introduces the running water head as an input divisor into the intelligent prediction model while the forecasting range is extended, and the result is satisfactory.

关 键 词：水电站厂房 耦联振动 粒子群优化算法 最小二乘支持向量机 响应预测 

分 类 号：TV698[水利工程—水利水电工程]