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投稿时间:2023-07-08 修订日期:2023-09-11
投稿时间:2023-07-08 修订日期:2023-09-11
中文摘要: 主轴止推轴承是风机的关键部件,一旦发生故障,将导致机组遭受严重损失。为实现风电机组主轴止推轴承早期故障预警,及早采取维护措施从而避免故障的进一步扩大。本文以风机主轴止推轴承温度为研究对象,提出一种基于风电机组正常运行状态下数据采集与监视控制(SCADA)的Stacking故障提前预警模型。首先,本文利用四个单一模型的拟合优度与均方误差比对特征进行综合排序,得到4组不同数量梯度特征组合的数据集。其次,通过对单一模型的预测性能以及相关性进行分析,最终确定以XGBoost、LightGBM以及随机森林作为基学习器,XGBoost作为元学习器建立Stacking集成学习预测模型。实验结果表明,基于Stacking模型对主轴止推轴承温度进行预测效果最好,预测误差相较于基模型有明显提升。最后,计算模型温度预测的均方根误差(RMSE),并基于指数加权移动平均法(Exponential Weighted Moving Average,EWMA)设定主轴止推轴承正常状态下误差阈值。实验结果显示,本文建立的Stacking模型对风机主轴止推轴承故障至少可以提前6小时发出故障预警。
中文关键词: Stacking集成学习 SCADA 风电机组 主轴止推轴承 故障预警
Abstract:The main shaft thrust bearing is a crucial component of the wind turbine, and any failure can lead to severe losses for the entire unit. To achieve early fault warning for the main shaft thrust bearing of wind turbine generator sets and take timely maintenance measures to avoid further escalation of faults, this paper focuses on the temperature of the wind turbine''s main shaft thrust bearing. It proposes a Stacking-based fault early warning model using data acquisition and supervisory control (SCADA) under normal operating conditions of wind turbine generators. Firstly, this study utilizes the ratio of goodness of fit and mean square error of four individual models to comprehensively rank the features, resulting in four sets of datasets with different combinations of gradient features. Secondly, by analyzing the predictive performance and correlation of the single model, XGBoost, LightGBM, and Random Forest are selected as the base learners, with XGBoost as the meta-learner to establish the Stacking ensemble learning prediction model. Experimental results show that the Stacking model based on temperature prediction of the main shaft thrust bearing performs the best, with significantly improved prediction errors compared to the base learners. Finally, the root mean square error (RMSE) of the temperature prediction model is calculated, and the error threshold for the normal state of the main shaft thrust bearing is set based on the Exponential Weighted Moving Average (EWMA) method. Experimental results demonstrate that the Stacking model established in this paper can issue fault warnings for wind turbines'' main shaft thrust bearing at least 6 hours in advance.
keywords: Stacking ensemble learning SCADA wind turbine system main shaft thrust bearing fault warning
文章编号: 中图分类号:TM315 文献标志码:
基金项目:山东省高等学校教学研究与改革面上项目(M2018X066);鲁东大学“专创融合”课程建设重点项目(2021Z08)。
作者 | 单位 | |
宋思瑜 | 鲁东大学 数学与统计科学学院 | l72walozn2i@gmail.com |
林正文 | 鲁东大学 数学与统计科学学院 | |
赵薇 | 鲁东大学 数学与统计科学学院 | |
黄文广 | 华风数据(深圳)有限公司 | |
刘广臣* | 鲁东大学 数学与统计科学学院 | gch_liu@163.com |
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