S-transform based on compact support kernel

Zidelmal, Zahia; Hamil, Hocine; Moukadem, Ali; Amirou, Ahmed; Abdeslam, Djaffar Ould

doi:10.1016/j.dsp.2016.11.008

Cited by 18 publications

(15 citation statements)

References 25 publications

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“…Therefore, the ST related to p parameter may be calculated as x(t) f q 2π ⋅ e −t 2 f 2 /2 f 2q . (27) Moreover, a combined proposal following (22) introduced a new arrangement for parameter q [19]:…”

Section: Other Windowing Proposalsmentioning

confidence: 99%

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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: Other Windowing Proposalsmentioning

confidence: 99%

“…Still, other proposals have come up with differentiated solutions, such as [22] with a compact support kernel.…”

Section: Other Windowing Proposalsmentioning

confidence: 99%

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

Beuter

Oleskovicz

2020

IET signal process.

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“…Many Gaussian‐based kernels have been explored in literature 27‐31 . However, in practical implementation, the Gaussian kernel needs to be truncated, which results in a loss of accuracy 32 . Thus, for more accurate results, a compact support kernel is preferred for the forecasting of wind speed.…”

Section: S‐transform Using Compactly Supported Kernelmentioning

confidence: 99%

“…A comprehensive discussion about compactly supported kernel for ST has been carried out recently. A kernel with compact support, derived from the Gaussian, is used by Zidelmal et al 32 Band‐limited and time‐limited kernels were proposed by Singh and Pradhan 18 …”

Section: S‐transform Using Compactly Supported Kernelmentioning

confidence: 99%

Short‐term wind speed forecasting using S‐transform with compactly supported kernel

Kamath

Senapati

2020

Wind Energy

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This paper presents a modified S‐transform (ST) based on a compactly supported kernel. A version of Cheriet‐Belochrani (CB) kernel is chosen for this purpose. It is shown that the proposed modified S‐transform (CBST) offers better frequency resolution than the traditional ST. It is used to decompose the wind speed time series into frequency‐based subseries. Further, artificial neural network (ANN) is applied to each of the subseries for an hour ahead prediction. Finally, forecast for the original wind speed series is obtained by combining the prediction result of all the subseries. Initially, increasing the number of subseries results in a decrease in prediction error. However, when the number of subseries is sufficiently large, no significant change in prediction error is observed if the number is further increased. It is also observed that, for a model based on neural‐network, involving decomposition of wind speed time series, the proposed model offers low prediction error. A comparative study with the methods based on wavelet transform (WT) and empirical mode decomposition (EMD) demonstrates the effectiveness of the proposed method. For this study, we have used simulated wind speed data generated by nonhydrostatic mesoscale model and data recorded using anemometer and LiDAR instrument at different heights to evaluate the short‐term forecasting results.

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“…Among them, the EMBD and the SCB TFD proposed in [16,22], such that an independent smoothing is performed along the frequency axis and the time axis, separately. Some recent TF representations based on S-transform combined with KCS are also investigated [23].…”

Section: The Cohen's Class-the Pcb Distribution (Pcbd)mentioning

confidence: 99%

On the Use of High-resolution Time-frequency Distribution Based on a Polynomial Compact Support Kernel for Fault Detection in a Two-level Inverter

Seninete

Abed

Bendiabdellah

et al. 2020

Period. Polytech. Elec. Eng. Comp. Sci.

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Quadratic Time-Frequency Distributions (TFDs) become a standard tool in many fields producing nonstationary signatures. However, these representations suffer from two drawbacks: First, bad time-frequency localization of the signal's autoterms due to the unavoidable crossterms generated by the bilinear form of these distributions. This results on bad estimation of the Instantaneous Frequency (IF) laws and decreases, in our case, the ability to precisely decide the existence of a motor fault. Secondly, the TFD's parameterization is not always straightforward. This paper deals with faults' detection in two-level inverter feeding induction motors, in particular open-circuit Insulated Gate Bipolar Transistor (IGBT) faults. For this purpose, we propose the use of a recent high-resolution TFD, referred as PCBD for Polynomial Cheriet-Belouchrani Distribution. The latter is adjusted using only a single integer that is automatically optimized using the Stankovic concentration measure, otherwise, no external windows are needed to perform the highest time-frequency resolution. The performance of the PCBD is compared to the best-known quadratic representations using a test bench. Experimental results show that the frequency components characterizing open-circuit faults are best detected using the PCBD thanks to its ability to suppress interferences while maintaining the signal's proper terms.

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S-transform based on compact support kernel

Cited by 18 publications

References 25 publications

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

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

Short‐term wind speed forecasting using S‐transform with compactly supported kernel

On the Use of High-resolution Time-frequency Distribution Based on a Polynomial Compact Support Kernel for Fault Detection in a Two-level Inverter

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