Based on the support vector regression(SVR)and the back-propagation(BP)neural network algorithms,and the practical production experience,correlation mapping is carried out between the bearing bush temperature and the major factors affecting the temperature variations during the operation of hydropower units.A correlation mapping model is then established.By comparing the prediction accuracy of the models,the optimal model is selected to predict the re-al-time bearing bush temperature.It realizes the intelligent and real-time prediction of the bearing bush temperature in hydropower units,and solves the shortcomings of the traditional monitoring scheme that the judgement information in threshold warning is limited.
关键词
支持向量回归/反向传播神经网络/水电机组/轴瓦温度/预测
Key words
support vector regression/back-propagation neural network/hydropower unit/bearing bush temperature/prediction
维普
万方数据