Fault detection and isolation in wind turbines using support vector machines and observers

被引：0

作者：

Sheibat-Othman, Nida ^{[1
]}

Othman, Sami ^{[1
]}

Benlahrache, Mohamed

Odgaard, Peter F.

机构：

[1] Univ Lyon, LAGEP, Lyon, France

来源：

2013 AMERICAN CONTROL CONFERENCE (ACC) | 2013年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this work, the benchmark FAST that simulates a closed-loop three-bladed wind turbine is used for fault detection and isolation. Two methods were employed to isolate faults of different types at different locations: Support vector machines (SVM) and a Kalman-like observer. SVM could isolate most faults with the used data and characteristic vectors, except for high varying dynamics. In this case, the use of an observer, which is model-based, was found necessary.

引用

页码：4459 / 4464

页数：6

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