Highly accurate detection of power quality disturbance using segmented and modified S-transform

Power quality (PQ) is important for the normal operation of modern power grids. The accurate detection of PQ disturbances is the basis for dealing with PQ events in power systems. This paper proposes an algorithm based on the segmented and modified S-transform (SMST) to accurately detect PQ disturbances. The SMST uses a modified Gaussian window with two parameters that can be adaptively adjusted according to the intrinsic characteristics of the PQ signals. Furthermore, the idea of segmentation is used in the modified S-transform, whereby the range of frequency of the signals is divided into three parts, i.e., low-, medium-, and high-frequency bands. The two adjustable parameters can then be accordingly selected in different frequency bands. This enables the SMST to adaptively satisfy different requirements of detection at different frequency bands. Finally, the results of detection of the proposed model were compared with those of prevalent algorithms in the area through simulations. They showed that it can significantly increase the time-frequency resolution in different frequency bands, and has superior accuracy of detection.

Automated summary

This paper proposes an algorithm based on the segmented and modified S-transform (SMST) to accurately detect PQ disturbances. The algorithm utilizes a modified Gaussian window with adaptive parameters and the idea of segmentation to satisfy different detection requirements in different frequency bands, resulting in superior accuracy of detection.