A.A. Mohd. Zin, H.H. Goh, and K.L. Lo
Power quality disturbances, wavelet transform analysis, Paul, Gaus-
sian, and Daubechies wavelet transform, uniqueness characterize
In this paper, a few approaches to detect, localize, and investigate the
feasibility of classifying various types of power quality disturbances
are presented. The approaches are based on wavelet transform
analysis, particularly the Paul, Gaussian, and Daubechies wavelet
transform. The key idea underlying the approaches are to decompose
a given disturbance signal into other signals which represents
transforming a one-dimensional time series into two-dimensional
time–frequency space. The decomposition is performed using the
Paul, Gaussian, and Daubechies wavelet transform techniques. The
techniques to detect and localize disturbances with actual power
line disturbances are proposed, and then demonstrated and tested.
In order to enhance the detection outcomes, the squared wavelet
transform coeﬃcients of the analyzed power line signal are utilized.
Based on the results of the detection and localization, an initial
investigation of the ability to uniquely characterize various types of
power quality disturbances is carried out. This investigation is based
on characterizing the uniqueness of the squared wavelet transform
coeﬃcients for each power quality disturbance.