Empirical wavelet transform for harmonic detection under dynamic condition

The wavelet transform has received great importance in the last years on the power system analysis because the multi-resolution analysis presents proprieties good for the transient signal analysis. This chapter presents a review on main application of wavelet transform in electric power systems. The study areas have been classified as power system protection, power quality disturbances, power system transient, partial discharge, load forecasting, faults detection, and power system measurement. The areas in which more works have been developed are the power quality and protections field, where both cover 51% of the articles analyzed.

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“…Ref. [24] proposes an empirical wavelet transform for harmonic detector under dynamism conditions of the system. Ref.…”

Section: Transient In Electrical Systemmentioning

confidence: 99%

Wavelet Transform Analysis to Applications in Electric Power Systems

Oliveira¹,

Reversat²,

Reynoso³

2019

Wavelet Transform and Complexity

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“…To tackle these limitations, inspired by (wavelet transform, WT) and EMD, Gilles proposed empirical wavelet transform (EWT) [12], which is considered one of the most powerful signal processing algorithms [13][14][15]. The main idea of EWT is to determine Fourier segments and then build a series of wavelet filters capable of extracting different modes from processed signals.…”

Section: Introductionmentioning

confidence: 99%

Novel noise reduction method based on improved empirical wavelet transform and kurtosis for partial discharge signal of high‐voltage cables

Xing-he

Zhang

2020

J. eng.

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Recent Advancements in Empirical Wavelet Transform and Its Applications

Liu

Chen

2019

IEEE Access

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Empirical wavelets transform (EWT) is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition (EMD) but has a consolidated mathematical theory, and is appealing in designing automatic algorithm for a variety of signal processing tasks. EWT first estimates the frequency components presented in the given signal, then, computes the boundaries and extracts the oscillatory components based on the computed boundaries. Because of the excellent performance of the EWT in decomposing the nonlinear and non-stationary signals, it has been successfully applied into a number of problems. The last six years have seen the development of EWT. This paper presents a general overview of the recent advancements made in research on the EWT algorithm and its state-of-the-art applications in a wide range of areas, such as machine fault diagnosis, seismic data analysis, image processing, power system monitoring, and medical disease diagnosis, which aims at providing some comprehensive references for reader concerning with EWT. We place emphasis on the applications of using such signal-analysis algorithm throughout with illustrative examples. Finally, the potential avenues for the future trends and directions associated with EWT are discussed.INDEX TERMS Empirical wavelet transform, machine health monitoring, seismic data analysis, signal and image processing, power system signals analysis, medical disease diagnosis.

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Empirical wavelet transform for harmonic detection under dynamic condition

Cited by 7 publications

References 9 publications

Wavelet Transform Analysis to Applications in Electric Power Systems

Wavelet Transform Analysis to Applications in Electric Power Systems

Novel noise reduction method based on improved empirical wavelet transform and kurtosis for partial discharge signal of high‐voltage cables

Recent Advancements in Empirical Wavelet Transform and Its Applications

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