摘要
为掌握广西三岔水电站水轮机轴承温度输出特性,以该水电站的上下游水位差、导叶开度、浆叶角度作为输入样本,推力轴承作为输出样本,在现场监测数据的基础上,利用BP神经网络与Matlab相结合,建立水轮机轴承温度输出特性模型。结果表明,所建立的模型具有很高的计算精度,有利于水电站水轮发电机组的优化运行系统开发。
To understand turbine bearing temperature output characteristics of Sancha hydropower station in Guangxi Zhuang Autonomous Region,the model of turbine bearing temperature output characteristics was developed using BP neural network and Matlab with on-site measuring data.Input sample of the model was water head difference between upstream and lower stream,gate-opening and blade angle,out sample was thrust bearing.Results showed that the ANN model had high accuracy,and could be used in optimizing hydroelectric generating set of hydropower station.
作者
杨海燕
韩国松
YANG Hai-yan;HAN Guo-song(Hebei University of Engineering,Handan 056038,Hebei,China)
出处
《水利科技与经济》
2020年第4期63-66,共4页
Water Conservancy Science and Technology and Economy
关键词
人工神经网络
水轮机
轴承温度
artificial neural network
turbine
bearing temperature
