Dynamic data window fault detection method based on relative principal component analysis

被引：0
|
作者：
Wang, Tianzhen [1 ,2 ]
Liu, Yuan [1 ]
Tang, Tianhao [1 ]
Chen, Yan [1 ]
机构：
[1] Shanghai Maritime University, Shanghai 200135, China
[2] Naval Academy Research Institute of France, Brest 29240, France
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2013年 / 28卷 / 01期
关键词：
Wind power - Fault detection - Electric power generation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In traditional principal component analysis(PCA), because of the neglect of the influence of dimension standardization, it was difficult to extract principal components(PCs) effectively. The fault detection method based on relative principal component analysis(RPCA), its control limit is related to the number of PCs and confidence. For these problems, a dynamic data window method based on RPCA is proposed in this paper. The proposed method combined the traditional control limit and dynamic data window by introducing a weight. Finally, it is applied to wind power generation system, can detect failures effectively and reduce the rate of false alarm.
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页码：142 / 148
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