Time–Frequency Analysis:The S‐Transform

Moukadem, Ali; Abdeslam, Djaffar Ould; Dieterlen, Alain

doi:10.1002/9781118908686.ch2

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…The concentration of the energy resulting from a signal, when analysed by ST and STFT, allows a better comparison of the resulting energies in the time-frequency axes, as subsequently shown. Opposite to WT, phase information provided by ST is connected to origin in time, using FT as a basis, which is not possible with continuous wavelet transform, where phase information is locally referenced [8,9]. The following section presents DST, its algorithm on the optimised form, as well as some final comparisons between ST and conventional methods as STFT and Wigner distribution function (WDF).…”

Section: S-transformmentioning

confidence: 99%

S‐transform: from main concepts to some power quality applications

Beuter

Oleskovicz

2020

IET signal process.

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Among various spectral analysis tools arisen in the last years, some were more prominent, such as Fourier transform, windowed Fourier transform and wavelet transform (WT). Nevertheless, all of them present implementation restrictions for an ideal extraction for low as well as high frequencies, of variant signals in time, as, for example, signals containing time-varying harmonics. In this sense, this study presents a review of concepts on the S-transform (ST), also known as Stockwell transform, applied in the analysis of some signals in the context of power quality (PQ). ST gathers, in a single function, positive qualities of both short time Fourier transform (STFT) and WT. This study presents a mathematical basis and some considerations regarding ST, referring to published papers, including a comparison between ST and STFT, as well as WT. It is worth emphasising that a final solution for the extraction of the low-and high-frequency information from time-varying signals is not yet available. ST is a satisfactory approach, but it still needs more detailed studies. This study presents a necessary set of steps for a better understanding of ST, reproduction of examples found in correspondent literature, as well as the ones regarding PQ.

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Section: S-transformmentioning

confidence: 99%

S‐transform: from main concepts to some power quality applications

Beuter

Oleskovicz

2020

IET signal process.

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Selection of the optimized window for the generalized s-transform using the AIW-PSO algorithm

Beuter

Oleskovicz

Golfieri

et al. 2018

2018 18th International Conference on Harmonics and Quality of Power (ICHQP)

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This work proposes a method for the selection of the optimized window of analysis for the Generalized S-Transform (GST), through an adaptation in the Particle Swarm Optimization (PSO) algorithm. The new version of the algorithm, called Adaptive Individual Inertia Weight Based on Best, Worst and Individual (AIW-PSO), uses an inertia weight, where the performance of the best and the worst particle are employed in the adaptive process. The GST is applied to the signal by using AIW-PSO, whose objective function to be minimized is the concentration of energy that will provide the proposed window selection. To evaluate the efficacy of the proposed method, two synthetic signals were analysed by the GST, based on parameters r, m, p and k, and compared with the standard version and an optimized version of the S-Transform (ST) available in the literature. It was found that the GST, optimized via the AIW-PSO algorithm, provided a better response in the Time-Frequency Representation (TFR) when compared to the standard non-ideal ST window, as well as an additional gain in energy concentration relative to the optimized version approach.

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Time–Frequency Analysis:The S‐Transform

Cited by 2 publications

References 38 publications

S‐transform: from main concepts to some power quality applications

S‐transform: from main concepts to some power quality applications

Selection of the optimized window for the generalized s-transform using the AIW-PSO algorithm

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