一种风电机组轴承健康劣化趋势预测方法  

A Method for Predicting Bearing Health Degradation Trend of Wind Turbines

作　　者：董兴辉[1] 张光[1] 程友星 王帅[2] DONG Xinghui;ZHANG Guang;CHENG Youxing;WANG Shuai(School of Energy, Power Beijing 102206, China;Unive and Mec School of hanical Engineering, Electrical Engineerin North China Electric Power University, g and Automation, Henan Polytechnic rsity, Jiaozuo 454000, Henan Province, China)

机构地区：[1]华北电力大学能源动力与机械工程学院,北京102206 [2]河南理工大学电气工程与自动化学院,河南焦作454000

出　　处：《动力工程学报》2018年第5期374-379,393,共7页Journal of Chinese Society of Power Engineering

基　　金：河北省科技计划资助项目(15214370D);北京市科技计划课题资助项目(2015BJ0220);国家自然科学基金资助项目(61573139)

摘　　要：以风电机组轴承为研究对象,利用SCADA监测有关参数,计算这些参数与轴承温度的相关系数,对其归一化后得到各参数的影响权重,然后基于温度特征量构建轴承健康劣化度模型。应用改进的集合经验模态分解(EEMD)将具有非平稳性特性的劣化趋势分解为一系列相对平稳的分量,采用时间序列神经网络模型分别预测各类分量,叠加所有预测分量得到最终预测结果。结果表明:本文方法的预测精度较高,提高了与实际劣化度曲线的吻合程度。Taking the bearing of a wind turbine as an object of study, using the monitored parameters by supervisory control and data acquisition （SCADA）, the correlation coefficient between monitored parame- ters and bearing temperatures was calculated and normalized to obtain the influence weight, and subsequently a model for degradation trend of bearing health was established. The degradation trend with nonsteady characteristics was decomposed by modified ensemble empirical mode decomposition （EEMD） to acquire several relatively steady components, and then each component was predicted by time series neural network, and final prediction results were formed by superposition of the predicted results of all components. Results show that the method proposed has higher prediction accuracy, which helps to improve the degree of conformity with actual degradation curves.

关 键 词：风电机组 轴承 劣化度 相关系数 EEMD 时间序列神经网络 

分 类 号：TM315[电气工程—电机]