可再生能源2018，Vol.36Issue(2)：276-282,7.
基于轴承温度模型的风电机组故障预测研究
The fault prediction in wind turbine based on temperature model of bearings
摘要
Abstract
Condition monitoring of wind turbines is important for ensuring the safety and stabilization of turbines,the bearing is a key part of energy transfer,whose condition estimation is signficant for turbines. A better linear regression RBF algorithm was applied to constructing the normal behavior model of the bearing temperature on account of principal component analysis. On this basis,the temperature was monitored according to the data in Supervisory Control And Data Acquisition system,with the aid of sliding window statistics. The results shows that when the generator is abnormal,an upward trend of the temperature is shown,and the residual value would exceed the setted threshold interval,the fault is predicted. This study can provide a reference for the safe and efficient operation of wind turbines.关键词
风电机组/轴承温度/线性回归RBF神经网络/残差/故障预测Key words
wind turbine/bearing temperature/linear regression RBF neural network/residual/fault prediction分类
能源科技引用本文复制引用
丁佳煜,许昌,葛立超,杨杰,许帅,李云涛..基于轴承温度模型的风电机组故障预测研究[J].可再生能源,2018,36(2):276-282,7.基金项目
国家自然科学基金项目(51507053) （51507053）
中央高校基本科研业务费项目(2017B42314). （2017B42314）
