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考虑多时间尺度信息的风力发电机滚动轴承故障预测

Fault Prediction of Wind Turbine Rolling Bearing Considering Multi-time Scale Information
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摘要 风电机组滚动轴承故障会造成风电机组长时间停机,为准确预测风电机组滚动轴承故障,提出一种考虑多时间尺度信息的风力发电机滚动轴承故障预测方法。首先,采用连续变分模式分解(successive variational mode decomposition,SVMD)自适应提取轴承健康数据温度多维特征;其次,将分解的本征模态函数(intrinsic mode functions,IMFs)输入Informer模型提取多尺度时间信息训练,基于树状结构Parzen密度估计的非标准贝叶斯优化算法(tree structure Parzen density estimation,TPE)优化Informer模型超参数;然后,构建基于残差的故障指标,采用核密度估计(kernel density estimation,KDE)确定故障预警阈值;最后,将运行数据输入训练后的Informer模型进行故障预测。选取某风电场的风力发电机轴承温度数据进行故障预测,仿真结果表明,考虑多时间尺度信息的SVMD-TPE-Informer模型在发电机轴承温度预测上具有更高的预测精度和计算效率,所提方法在两个故障案例上分别能够提前15.5 h和10 h预测到故障,且不会出现误报现象,验证所提模型的有效性和稳定性。 The fault of wind turbine rolling bearing will cause long-term downtime.To accurately predict the fault of wind turbine rolling bearing,this paper proposes a fault prediction method of wind turbine rolling bearing considering multi-time scale information.First,successive variational mode decomposition(SVMD)is used to adaptively extract the multi-dimensional characteristics of bearing health data.Next,the decomposed IMFS is input into the Informer model to extract multi-scale time information for training,and the nonstandard Bayesian optimization algorithm based on tree structure Parzen density estimation(TPE)is used to optimize the super parameters of the Informer model.Then,the fault index based on residual is constructed,and the fault early warning threshold is determined by kernel density estimation(KDE).Finally,the running data is input into the trained Informer model for fault prediction.In this paper,the bearing temperature data of a wind farm are selected for fault prediction.The simulation results show that the SVMD-TPE-Informer model considering multi-time scale information has higher prediction accuracy and calculation efficiency.The proposed method can predict faults with a lead time of 15.5 hours and 10 hours respectively in two fault cases,without any false alarm,which verifies the effectiveness and stability of the proposed model.
作者 赵洪山 林诗雨 孙承妍 杨伟新 张扬帆 ZHAO Hongshan;LIN Shiyu;SUN Chengyan;YANG Weixin;ZHANG Yangfan(School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,Hebei Province,China;North China Electric Power Research Institute Co.,Ltd.,Xicheng District,Beijing 100045,China)
出处 《中国电机工程学报》 EI CSCD 北大核心 2024年第22期8908-8919,I0018,共13页 PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING
基金 国家自然科学基金项目(51277074) 国家电网有限公司科技项目(52018K2001P)。
关键词 连续变分模式分解 贝叶斯优化 Informer模型 故障预测 successive variational mode decomposition(SVMD) Bayesian optimization informer model failure prediction
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