基于RVM方法的水电站厂房结构振动预测研究

随着水电站规模和单机容量的不断增长,水电站厂房振动问题日益突出。明确厂房结构的振动规律有助于电站长期运行安全评估。在电站厂房原型振动观测数据相关分析的基础上,建立了基于相关向量机(Relevance Vector Machine,RVM)的厂房振动响应预测模型。该模型可通过机组、流道测点的测试数据预测厂房结构垂直振动空间分布,并具有较高的精度。

Structural vibration prediction for a hydropower house based on RVM method

Along with the increase of scale of hydropower station and single unit capacity, the vibration problems of hydropower houses are becoming acute. It is useful to assess a hydropower station safe, if the structural vibration characteristics of a hydropower house can be known. In this paper, basing on correlation analysis of the prototype observation vibration data of a huge underground power plant, the vibration prediction model of the hydropower house was established using relevance vector machine method (RVM). With the model, the vertical vibration responses of the powerhouse can be predicted by the vibration data of units and pressure pulsation data of the draft tube. The results show that the prediction model has high accuracy.