A wavelet “time-shift-detail” decomposition

Levan, N.; Kubrusly, Carlos S.

doi:10.1016/s0378-4754(03)00037-5

Cited by 17 publications

(4 citation statements)

References 5 publications

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“…≠ 0, where 𝑚 ′ , 𝑛 ′ , 𝑗, 𝑘 ∈ Z (see e.g. Levan and Kubrusly [2003] and Mallat [2008]). Based on Kubrusly and Levan [2006], it can be seen that the subset 𝜔 𝑚,𝑛 (.)…”

Section: Wavelet Decomposition Of Level Rmentioning

confidence: 99%

Applications of Numerical and Statistical Methods for Monitoring and Control Engineering Variables in a Concrete Dam

Lazzarotto¹,

Oro²,

Reinaldo³

et al. 2022

Uncertainty Modeling: Fundamental Concepts and Models

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This chapter presents a compilation of techniques and research results published by the authors in journal articles concerning the use of numerical methods and univariate and multivariate statistical methods applied to evaluate, predict and control the behavior of important engineering variables that are monitored by instruments in the dam of the Itaipu hydroelectric plant. The introductory section contextualizes Itaipu and the importance of the methods used. Next, we present a brief theoretical description of univariate forecasting methods, multivariate statistical techniques, and the finite element method. After describing the set of techniques, the reader can always find specific applications in Itaipu of one or more of the preceding techniques. The results, in general, show that the combination of statistical and numerical methods helps to improve the performance of forecasts and, in general, can collaborate in the decision-making of Itaipu engineers and are an additional risk analysis tool that can be applied to any dam.

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Section: Wavelet Decomposition Of Level Rmentioning

confidence: 99%

Applications of Numerical and Statistical Methods for Monitoring and Control Engineering Variables in a Concrete Dam

Lazzarotto¹,

Oro²,

Reinaldo³

et al. 2022

Uncertainty Modeling: Fundamental Concepts and Models

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“…Wavelet transform (WT) is a time-frequency tool that can decompose the input signal to different frequency bands [13]. This transform has found many applications in power systems, such as harmonic detection and power calculation [14,15].…”

Section: Wavelet Transformmentioning

confidence: 99%

Improving the SRF method to compensate low-order harmonics under nonsinusoidal network voltages

Modarresi

Fallah²,

Ghokipour³

et al. 2016

Turk J Elec Eng & Comp Sci

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Increasing the application of power in electronic devices has increased the harmonics in power systems. Numerous methods like the synchronous reference frame (SRF) and the p-q-based method have been suggested to overcome the effects of these harmonics. The conventional SRF method provides acceptable results in harmonic compensation of high-order harmonics (higher than the fourth order), but the transient response time will be drastically increased in the presence of low-order harmonics due to the existence of a conventional low-pass filter. Furthermore, if the load terminal voltages are distorted, then the conventional SRF method will become unable to implement load current compensation. This research has used wavelet transform to overcome these difficulties. The proposed method is not only faster than conventional SRF, but it can also compensate the load currents if the load terminal voltages are also distorted. The simulation and experimental results are performed using MATLAB/Simulink and digital signal processor TMS320F28335 to verify the proposed method.

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“…Firstly, Steps 1 and 4 are aimed to obtain a finite number of temporal subseries (WCs) with better stochastic pattern than the underlying time series. It is possible due to the fact each WC has a stationary spectral frequency, as noted in Mallat (2009); while can be seen as the result of the sum of spectral components with different frequencies, as pointed by Levan and Kubrusly (2003).…”

Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Time Series Forecasting by Using a Neural Arima Model Based on Wavelet Decomposition

Pereira

Scarpin

Júnior

2016

Ind. Jour. Man. & Prod.

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In the prediction of (stochastic) time series, it has been common to suppose that an individual predictive method-for instance, an Auto-Regressive Integrated Moving Average (ARIMA) model-produces residuals like a white noise process. However, mainly due to the structures of auto-dependence not mapped by a given individual predictive method, this assumption might be easily violated, in practice, as pointed out by Firmino et al. (2015). In order to correct it

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A wavelet “time-shift-detail” decomposition

Cited by 17 publications

References 5 publications

Applications of Numerical and Statistical Methods for Monitoring and Control Engineering Variables in a Concrete Dam

Applications of Numerical and Statistical Methods for Monitoring and Control Engineering Variables in a Concrete Dam

Improving the SRF method to compensate low-order harmonics under nonsinusoidal network voltages

Time Series Forecasting by Using a Neural Arima Model Based on Wavelet Decomposition

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