基于PSO优化LS-SVM算法的水电站厂房结构振动响应预测

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。...

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Bibliographic Details
Published in中国工程科学 Vol. 13; no. 12; pp. 45 - 50
Main Author 练继建 何龙军 王海军
Format Journal Article
LanguageChinese
Published 中国工程院战略咨询中心 01.12.2011
高等教育出版社有限公司
天津大学水利工程仿真与安全国家重点实验室,天津,300072
Subjects
响应预测
最小二乘支持向量机
水电站厂房
粒子群优化算法
耦联振动
最小二乘支持向量机
耦联振动
水电站厂房
粒子群优化算法
响应预测
Online AccessGet full text
ISSN1009-1742
2096-0034
DOI10.3969/j.issn.1009-1742.2011.12.008

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Summary: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。
Bibliography:powerhouse; coupled vibration; particle swarm optimization algorithm; least squares supportvector machines; response prediction
11-4421/G3
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.
Lian Jijian, He Longjun,Wang Haijun （ State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China）
ISSN:1009-1742
2096-0034
DOI:10.3969/j.issn.1009-1742.2011.12.008