A fault detection method based on dynamic peak-valley limit under the non-steady conditions

作者：Wang Tian-Zhen; Xu Man; Tang Tian-Hao; Claramunt Christophe

来源：WSEAS Transactions on Circuits and Systems, 2013, 12(4): 118-128.

• Data dimension reduction
• Fault detect
• MLS
• Multivariate statistical method
• Non-steady conditions
• Peak-valley Curve
• RPCA
• Wind power generation systems

The multivariate statistical methods are commonly used to fault detection through a straight limit line given by the HotellingT2. However, the traditional straight limit line is difficult to detect the fault effectivelyunder the non-steady conditions, and the rate of false alarmand missing alarm is high. For these problems above, a fault detection method based on dynamic peak-valley limit is proposed in this paper. The proposed method introduces relative principal component analysis (RPCA) to carry out data dimension reduction, extractprincipal component (PCs) and calculate T 2statistics, then adopts moving least squares(MLS) to preprocessT2statistics to obtain the fitting curve which is called peak-valley curve, and finally connects peak and valley points in the curve to construct another control limit, by introducing a weight combined with the traditional straight limit line to construct the dynamic peak-valley limit. At the end, it is applied to wind power generation system, and the results could verify the effectiveness of the method.

• 出版日期2013
• 单位上海海事大学