水电站尾水位特性解析与建模  

Analysis and modeling of tailwater level characteristics of hydropower stations

作　　者：贾本军 周建中[1,2] 陈潇[1,2] 张勇传[1,2] 田梦琦 JIA Benjun;ZHOU Jianzhong;CHEN Xiao;ZHANG Yongchuan;TIAN Mengqi(School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan 430074;Hubei Key Laboratory of Digital Valley Science and Technology,Huazhong University of Science and Technology,Wuhan 430074)

机构地区：[1]华中科技大学土木与水利工程学院,武汉430074 [2]华中科技大学数字流域科学与技术湖北省重点实验室,武汉430074

出　　处：《水力发电学报》2021年第10期45-59,共15页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金重点支持项目(U1865202,52039004);国家重点研究计划课题(2016YFC0402205)。

摘　　要：准确解析水电站的尾水位特性是对其进行建模、从而实现尾水位高精度预测的关键。首先采用定性与定量分析相结合的方法揭示了水电站尾水位变化的后效性特征;然后基于相关性分析初步探明了尾水位变化过程的关键影响因子;进一步构建了水电站尾水位特性的多项式拟合模型和支持向量回归模型,并对比分析了各模型描述水电站尾水位特性的性能。溪洛渡-向家坝梯级和三峡-葛洲坝梯级水电站的实例研究表明,四座水电站2小时尺度的尾水位变化过程后效性特征显著,以当前和前一时段的下泄流量以及下游电站水位或下游支流来水为输入的支持向量回归模型是一种实用性、可靠性和准确性均衡的具有实际工程应用价值的尾水位预测模型。This paper focuses on how to analyze and model accurately the tailwater level characteristics of hydropower stations,so as to achieve high accuracy predictions.First,we reveal the aftereffect characteristics of tailwater level variations at hydropower stations by combining qualitative and quantitative analysis,and explore preliminarily the key influencing factors of the variations based on a Pearson correlation analysis.Then,we construct a polynomial fitting model and a support vector regression model,and compare and analyze their performances in prediction of tailwater level variations Case studies of the Xiluodu-Xiangjiaba cascade and Three Gorges-Gezhouba cascade show a significant aftereffect is produced by a two-hour variation in the tailwater stages of the four hydropower stations.And the support vector regression model with reservoir discharge of the present and previous periods and reservoir tailwater stage or downstream tributary discharge as inputs is a practical,reliable and accurate in prediction of the tailwater levels.

关 键 词：水电站 尾水位特性 尾水位预测 相关性分析 多项式拟合 支持向量回归 

分 类 号：TV697[水利工程—水利水电工程]