风电机组轴承健康劣化趋势建模与仿真

doi:10.16182/j.issn1004731x.joss.17-0067

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (1): 151-158.doi: 10.16182/j.issn1004731x.joss.17-0067

风电机组轴承健康劣化趋势建模与仿真

董兴辉¹, 马晓双¹, 程友星¹, 王帅²

1. 华北电力大学能源动力与机械工程学院,北京 102206;
2. 河南理工大学电气学院,河南焦作 454000

收稿日期:2017-01-18 修回日期:2017-08-21 出版日期:2019-01-08 发布日期:2019-04-16
作者简介:董兴辉(1962-),男,河南沁阳,博士,教授,研究方向为风电机组状态评估、故障识别等;马晓双(1990-),女,山东蒙阴,硕士,研究方向为风电机组状态评估、故障识别。
基金资助:
国家重点研发计划(2017YFE0109000)

Modeling and Simulation of Health Degradation Trend for Wind Turbine Bearing

Dong Xinghui¹, Ma Xiaoshuang¹, Cheng Youxing¹, Wang Shuai²

1. School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China;
2. School of Electric Engineering, Henan Polytechnic University, Jiaozuo 454000, China

Received:2017-01-18 Revised:2017-08-21 Online:2019-01-08 Published:2019-04-16

摘要/Abstract

摘要： 以风电机组轴承为研究对象,利用SCADA(Supervisory Control And Data Acquisition)监测参数,应用最小二乘曲面拟合算法,建立轴承温度健康状态劣化趋势模型。改进并应用EEMD (Ensemble Empirical Mode Decomposition)方法,分解具有非平稳性特性的轴承劣化趋势为一系列相对平稳的分量,利用时间序列神经网络分别对各分量单独预测,叠加所有分量的预测值作为最终的预测结果。经过仿真测试,该方法能够以更高的精度预测风电机组轴承健康状态劣化趋势。

关键词: 风电机组轴承, 劣化趋势预测, 最小二乘法曲面, EEMD方法, 时间序列神经网络

Abstract: By taking a wind turbine bearing as research object, the model of bearing temperature health’s degradation trend is established through using least squares surface fitting and the monitored parameters from Supervisory Control And Data Acquisition (SCADA). Bearings’ degradation trend with unsteady characteristics is decomposed by modified Ensemble Empirical Mode Decomposition(EEMD) to obtain several relatively steady components. Components are predicted respectively by time series neural network and the predicted results of all the components are added to obtain final prediction result. Comprehensive simulations and comparisons show that the proposed method can predict the health degradation trend of wind turbine bearings with higher accuracy.

Key words: wind turbine bearing, degradation trend prediction, least squares surface, EEMD, time series neural network

中图分类号:

董兴辉, 马晓双, 程友星, 王帅. 风电机组轴承健康劣化趋势建模与仿真[J]. 系统仿真学报, 2019, 31(1): 151-158.

Dong Xinghui, Ma Xiaoshuang, Cheng Youxing, Wang Shuai. Modeling and Simulation of Health Degradation Trend for Wind Turbine Bearing[J]. Journal of System Simulation, 2019, 31(1): 151-158.

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风电机组轴承健康劣化趋势建模与仿真

Modeling and Simulation of Health Degradation Trend for Wind Turbine Bearing

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