Lifting scheme‐based matched wavelet design for effective characterisation of different types of voltage sag

Abstract

This study presents a novel scheme for designing biorthogonal wavelets matched to the voltage sag (VS) signals. Instead of using standard wavelets, matched wavelets designed in this study can extract more distinguishing features for better characterisation of sag signals, although this study proposes only the designing process of matched wavelets. The key method used for designing matched wavelets is the lifting scheme, which has never been reported in the literature for constructing matched wavelets for PQ signals. Besides using the lifting scheme, the proposed work preprocesses sag signals to make input signal of lifting scheme rich of sag-related information. During pre-processing, 1-D sag signal is first converted to 2-D form for capturing information of every small transition (which may be overlooked in 1-D form) and then made 1-D by ring projection to implement 1-D lifting scheme. In this way, biorthogonal wavelets have been well matched to every type of VS signal collected through the experimental setup simulating different line faults, induction motor starting, transformer energisation and heavy load starting. Also, the characteristics of designed wavelets are evaluated in a MATLAB environment using frequency response and pole-zero mapping in z-plane. Higher PSNR values during signal reconstruction proved outperformance of newly designed matched wavelets.How to cite this article: Aggarwal A, Saini MK. Lifting scheme-based matched wavelet design for effective characterisation of different types of voltage sag.

A systematic review of real-time detection and classification of power quality disturbances

“…Discrete WT (DWT) is the discrete realization of WT and has been widely used for detection and classification of multiple PDQs as the main transformation technique [33, 34, 37-39, 42, 44-46, 48, 51, 53, 55, 59, 60, 62-64, 69, 76, 83, 95, 101, 110, 123] or combined with other techniques [35,36,41,61,73,80,102,117,126]. It has also been used as the main transformation technique for the detection and classification of voltage sags [133,142,146,154,168,169] and notches [187, 190, 191, 193, 194, 197-199, 204, 208], or combined with other techniques for voltage sags [160,181,186] and notches [189,192,196,202]. DWT is given by [33]:…”

“…Decomposition (SSD) on an overcomplete hybrid dictionary matrix [87], Singular Spectrum Analysis (SSA) along with Curvelet technique [103], 2D image techniques (gray-scale images) [112,179,181], numerical pencil [134], matrix pencil [153], and Goertzel method [166]. The evolution of transformation techniques is described in Fig.…”