The multi-core parallel algorithms of wavelet/wavelet packet transforms and their applications in power system harmonic analysis and data compression

Liu, Zhigang; Geng, Xiao; Xie, Zuoming; Lu, Xiaofan

doi:10.1002/etep.1992

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Feature extraction in time frequency domain using parallel computing is explained in [175]. Another multi core algorithm for WT/WPT is proposed in [176], for harmonics analysis and data compression. The parallel processing of the algorithm reduces the computation time.…”

Section: E Miscellaneous Techniquesmentioning

confidence: 99%

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

et al. 2021

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Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its preeminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.

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Section: E Miscellaneous Techniquesmentioning

confidence: 99%

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

et al. 2021

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“…Several approaches have been reported to analyze harmonics, including wavelet transform [6,7], neural network [8,9], multiple signal classification (MUSIC) algorithm [10,11], fast Fourier transform (FFT)-based method [12,13] and so on. So far, it has been a difficult and important research direction to find a suitable wavelet basis function for harmonic analysis [14,15]. The neural network method is a hot topic, but it is seldom used in practice, since it needs a large amount of sample training [16,17].…”

Section: Introductionmentioning

confidence: 99%

Improved Parameter Identification Method for Envelope Current Signals Based on Windowed Interpolation FFT and DE Algorithm

Su¹,

Zhang²,

Chen³

et al. 2018

Algorithms

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Envelope current signals are increasingly emerging in power systems, and their parameter identification is particularly necessary for accurate measurement of electrical energy. In order to analyze the envelope current signal, the harmonic parameters, as well as the envelope parameters, need to be calculated. The interpolation fast Fourier transform (FFT) is a widely used approach which can estimate the signal frequency with high precision, but it cannot calculate the envelope parameters of the signal. Therefore, this paper proposes an improved method based on windowed interpolation FFT (WIFFT) and differential evolution (DE). The amplitude and phase parameters obtained through WIFFT and the envelope parameters estimated by the envelope analysis are optimized using the DE algorithm, which makes full use of the performance advantage of DE. The simulation results show that the proposed method can improve the accuracy of the harmonic parameters and the envelope parameter significantly. In addition, it has good anti-noise ability and high precision.

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“…But both techniques are limited in a fixed window length and come with heavy computation. Alternative algorithms are discrete wavelet transform , empirical mode decomposition (EMD) , and single channel independent component analysis . In , the authors managed to use different time‐frequency‐domain and time‐domain techniques to decompose voltage or current signals into several harmonics and disturbances and analyze each of them one by one.…”

Section: Introductionmentioning

confidence: 99%

A time-domain statistical approach for harmonics separation and analysis

Shu

Liu

et al. 2016

Int. Trans. Electr. Energ. Syst.

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The paper proposes a novel time-domain statistical method to separate and analyze harmonics based on single channel analysis and principal component analysis. The proposed method separates the fundamental wave and harmonics to provide specific time-domain information. Two identification methods are proposed to estimate the number of harmonics within the power system signal, even with high noise. This paper validates the effectiveness of the proposed method through mathematical deduction and studies the selection of Euclidean embedding dimension. The robustness of the proposed method is tested on several simulated synthetic signals and a real-world signal. Simulation results show that the proposed method can detect harmonics with small amplitude in the presence of noise and has better separated waveform and less execution time than wavelet-packet transform and empirical mode decomposition. It is also shown that the proposed method can detect sudden voltage changes and separate disturbances.On the basis of this complex exponential model, the next section discusses a time-domain statistical way to separate harmonics and recover their time-domain waveform. C. HE ET AL.

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The multi-core parallel algorithms of wavelet/wavelet packet transforms and their applications in power system harmonic analysis and data compression

Cited by 5 publications

References 26 publications

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

Improved Parameter Identification Method for Envelope Current Signals Based on Windowed Interpolation FFT and DE Algorithm

A time-domain statistical approach for harmonics separation and analysis

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