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电气工程与技术
Electrical Engineering and Technology
ISSN Print:2708-4434
ISSN Online:2708-4442
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基于SCADA数据的风机叶片故障检测技术研究与展望
Research and prospect of fan blade fault detection technology based on SCADA data
- 作者： 蒋志伟 李永坚
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单位：
湖南工程学院电气与信息工程学院，湘潭
- 关键词： 风机叶片；预处理技术；特征选择；模型；故障诊断
- Wind Turbine Blade; Pretreatment technology; Feature selection; Model; Fault diagnosis
- 摘要： 以基于SCADA数据的风机叶片故障检测技术为研究对象，首先对风机叶片故障类型和故障成因进行分析，并对近十年以来SCADA系统监控数据的预处理技术、叶片故障诊断模型的建立进行了研究，经过综合对比，本文建议采用交叉验证的递归特征消除法(RFECV)和基于随机森林算法的故障诊断模型来处理更多的叶片故障。最后对风机叶片故障检测技术的未来发展方向进行了总结和建议。
- Abstract: The fan blade fault detection technology based on SCADA data is taken as the research object. Firstly, the fault types and causes of fan blades are analyzed, and the pre-processing technology of SCADA system monitoring data and the establishment of blade fault diagnosis model in the past ten years are studied. After comprehensive comparison, In this paper, a cross-validated recursive feature elimination (RFECV) method and a fault diagnosis model based on random forest algorithm are proposed to deal with more blade faults. Finally, the future development direction of fan blade fault detection technology is summarized and suggested.
- DOI： https://doi.org/10.35534/eet.0203011
- 引用： 蒋志伟，李永坚．基于 SCADA 数据的风机叶片故障检测技术研究与展望［J］．电气工程与技术， 2021，2（3）：33-47．
