数据驱动的风电机组变桨系统状态监测

金晓航; 泮恒拓; 徐正国

doi:10.19912/j.0254-0096.tynxb.2020-0816

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (4) : 409-417. DOI: 10.19912/j.0254-0096.tynxb.2020-0816

电化学储能安全性与退役动力电池梯次利用关键技术专题

数据驱动的风电机组变桨系统状态监测

金晓航^1,2, 泮恒拓¹, 徐正国³

作者信息 +

CONDITION MONITORING OF WIND TURBINE PITCH SYSTEM USING DATA-DRIVEN APPROACH

Jin Xiaohang^1,2, Pan Hengtuo¹, Xu Zhengguo³

Author information +

文章历史 +

摘要

针对数据采集与监视控制（SCADA）系统存在误报、故障报警滞后等问题,提出一种基于单分类模型的风电机组变桨系统在线状态监测方法。首先,从SCADA数据中提取出与变桨系统相关的特征参数并进行特征重构以进一步提取出更值得关注的桨叶之间的差异化信息。其次,基于单分类支持向量机对历史数据的分析确定变桨系统运行数据的健康边界,进而通过判断实时运行数据是否位于健康边界内部来辨别变桨系统当前的运行状态。最后,以变桨系统的实际工程案例分析验证所提方法的有效性。

Abstract

To address the problems of a high false alarm rate and delayed fault alarm in supervisory control and data acquisition （SCADA） systems, an online condition monitoring method for wind turbine pitch system is proposed. Firstly, the parameters related to the pitch system are extracted from the SCADA data, and these parameters are reconstructed to extract the differential information between the blades. Secondly, the health boundary of the working status of pitch system is found based on the analysis of the historical data by using one-class support vector machine, and then the online health status of the pitch system can be assessed by judging whether the real-time data is within the health boundary. Finally, the effectiveness of the proposed method is verified by two actual engineering cases of the wind turbine’s pitch system.

导出引用

金晓航, 泮恒拓, 徐正国. 数据驱动的风电机组变桨系统状态监测[J]. 太阳能学报. 2022, 43(4): 409-417 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0816

Jin Xiaohang, Pan Hengtuo, Xu Zhengguo. CONDITION MONITORING OF WIND TURBINE PITCH SYSTEM USING DATA-DRIVEN APPROACH[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 409-417 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0816

中图分类号： TH17

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