Real-Time Power Measurement Using the Maximal Overlap Discrete Wavelet-Packet Transform

Alves, Dênis Keuton; Costa, F. B.; Ribeiro, R.L.A.; Neto, Cecílio Martins de Sousa; Rocha, Thiago de Oliveira Alves

doi:10.1109/tie.2016.2637304

Cited by 82 publications

(47 citation statements)

References 46 publications

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“…WPD algorithms continue to be interesting for power system researchers, as suggested by recent works [20,23], but in order to move forward and make WPD a feasible and better alternative to DFT, a complete algorithm for harmonic assessment must be designed. Modern power systems, with the increasing presence of non-linear loads, require a method compatible with the IEC standards, able to measure not only odd harmonics, but also even harmonics, and up to the highest possible order.…”

Section: Wavelet Transforms In Power Systemsmentioning

confidence: 99%

“…From WT, Wavelet Packet Decomposition (WPD) was derived, generalizing the link between wavelets and Multiresolution Analysis (MRA). Thanks to the uniform frequency bands that can be obtained with WPD, its application in Power Quality has been proposed for power calculation [20,21] and harmonic measurements [22][23][24]. However, the WPD harmonic analyses proposed in the literature do not look complete from the point of view of compliance with international standards and capabilities under stationary conditions, as will be discussed in Section 2.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet Packet Decomposition for IEC Compliant Assessment of Harmonics under Stationary and Fluctuating Conditions

et al. 2019

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This paper presents the validation and characterization of a wavelet based decomposition method for the assessment of harmonic distortion in power systems, under stationary and non-stationary conditions. It uses Wavelet Packet Decomposition with Butterworth Infinite Impulse Response filters and a decomposition structure, which allows the measurement of both odd and even harmonics, up to the 63rd order, fully compliant with the requirements of the IEC 61000-4-7 standard. The method is shown to fulfil the IEC accuracy requirements for stationary harmonics, obtaining the same accuracy even under fluctuating conditions. Then, it is validated using simulated signals with real harmonic content. The proposed method is proven to be fully equivalent to Fourier analysis under stationary conditions, being often more accurate. Under non-stationary conditions, instead, it provides significantly higher accuracy, while the IEC strategy produces large errors. Lastly, the method is tested with real current and voltage signals, measured in conditions of high harmonic distortion. The proposed strategy provides a method with superior performance for fluctuating harmonics, but at the same time IEC compliant under stationary conditions.

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Section: Wavelet Transforms In Power Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelet Packet Decomposition for IEC Compliant Assessment of Harmonics under Stationary and Fluctuating Conditions

et al. 2019

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“…The maximal overlap DWPT (MODWPT) has been used for the real-time estimation of root mean square (RMS) power value, active power, reactive power, apparent power, and power factor in power electronic systems [26].…”

Section: The Applications Of Wavelet Transformsmentioning

confidence: 99%

Applications of Wavelet Transforms to the Analysis of Superoscillations

Ezra¹,

Lembrikov²,

Schwartz³

et al. 2018

Wavelet Theory and Its Applications

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The phenomenon of superoscillation is the local oscillation of a band limited function at a frequency ω higher than the band limit. Superoscillations exist during the limited time intervals, and their amplitude is small compared to the signal components with the frequencies inside the bandwidth. For this reason, the wavelet transform is a useful mathematical tool for the quantitative description of the superoscillations. Continuoustime wavelet transform (CWT) of a transient signal ft ðÞis a function of two variables: one of them represents a time shift, and the other one is the scale or dilation variable. As a result, CWT permits the simultaneous analysis of the transient signals both in the time and frequency domain. We show that the superoscillations strongly localized in time and frequency domains can be identified by using CWT analysis. We use CWT with the Mexican hat and Morlet mother wavelets for the theoretical investigation of superoscillation spectral features and time dependence for the first time, to our best knowledge. The results clearly show that the high superoscillation frequencies, time duration, and energy contours can be found by using CWT of the superoscillating signals.

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“…35 Therefore, a substitute method has been developed called Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) to provide a uniform frequency bandwidths and overcome time-variant transformation. 32,35 MODWPT decomposes the original signal into many unique components with different levels of resolution. MODWPT also allows the reconstruction of the original signal without losing any information and facilitates the monitoring of the gear state.…”

Section: Introductionmentioning

confidence: 99%

Gear fault diagnosis using Autogram analysis

Afia

Rahmoune

Benazzouz

2018

Advances in Mechanical Engineering

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Rotary machines consist of various devices such as gears, bearings, and shafts that operate simultaneously. As a result, vibration signals have nonlinear and non-stationary behavior, and the fault signature is always buried in overwhelming and interfering contents, especially in the early stages. As one of the most powerful non-stationary signal processing techniques, Kurtogram has been widely used to detect gear failure. Usually, vibration signals contain a relatively strong non-Gaussian noise which makes the defective frequencies non-dominant in the spectrum compared to the discrete components, which reduce the performance of the above method. Autogram is a new sophisticated enhancement of the conventional Kurtogram. The modern approach decomposes the data signal by Maximal Overlap Discrete Wavelet Packet Transform into frequency bands and central frequencies called nodes. Subsequently, the unbiased autocorrelation of the squared envelope for each node is computed to select the node with the highest kurtosis value. Finally, Fourier transform is applied to that squared envelope to extract the fault signature. In this article, the proposed method is tested and compared to Fast Kurtogram for gearbox fault diagnosis using experimental vibration signals. The experimental results improve the detectability of the proposed method and affirm its effectiveness.

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Real-Time Power Measurement Using the Maximal Overlap Discrete Wavelet-Packet Transform

Cited by 82 publications

References 46 publications

Wavelet Packet Decomposition for IEC Compliant Assessment of Harmonics under Stationary and Fluctuating Conditions

Wavelet Packet Decomposition for IEC Compliant Assessment of Harmonics under Stationary and Fluctuating Conditions

Applications of Wavelet Transforms to the Analysis of Superoscillations

Gear fault diagnosis using Autogram analysis

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