电能质量信号扰动检测与定位

Detection and Location of Power Quality Signal Perturbation

在P iella G等人提出的自适应提升小波变换的基础上,提出了有效分析电能质量信号扰动检测与定位的改进的自适应提升小波预测算法;根据改进的自适应提升小波对信号的极强分类能力,将经过派克变换后的电能质量信号分为正常区与扰动区以及间断点区与非间断点区,再对各不同的区运用不同的更新算法与预测算法,能够快速准确地对电能质量信号作出扰动检测定位以及扰动程度的定量刻划;并且根据测出的扰动时间及扰动方向可以对扰动类型做出准确的分类;运用该算法对电压凸起、电压凹陷、电压暂态中断、电压瞬时脉冲、电压暂态振荡的检测与定位以及电压谐波的分析都证实了该算法的准确性和有效性。

This paper puts forward to the improved auto-adapted promotion wavelet forecast algorithm of effectively analyzing power quality signal perturbation detection and location based on the auto-adapted promotion wavelet transformation proposed by Piella G and others.According to the strong classifying ability of improved auto-adapted promotion wavelet to the signal,the power quality signal will be divided into the normal part and the perturbation area as well as the point of discontinuity area and the non-point of discontinuity area after Parker transform,then the detection and location of perturbation as well as the quantification and the description of the perturbation degree toward power quality signal can be quickly and accurately carried on by using different renewal algorithm and the forecast algorithm to each different area.And the perturbation time and the perturbation direction can determine the accurate classification to perturbation type.Using this algorithm to the detection and location of the voltage bulge,voltage sag,voltage transient interrupt,voltage transient pulse as well as the voltage transient vibration,and analyzing the voltage harmonics all confirm the accuracy and validity of this algorithm.