Power parameters calculations based on wavelet packet transform

Vatansever, Fahri; Özdemir, Ayhan

doi:10.1016/j.ijepes.2009.04.001

Cited by 30 publications

(11 citation statements)

References 10 publications

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“…In other words, WPT decomposes the input signal into several frequency bands with the same length. Figure 3 shows the 2 decomposition level tree [22]. In WPT, the number of the extracted frequency bands at decomposition levelj (M j ) is obtained by Eq.…”

Section: Wavelet Packet Transformmentioning

confidence: 99%

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A new combined method for RMS calculation based on wavelet packet and Hilbert transform

Hooshmand¹,

Modarresi²

2016

Turk J Elec Eng & Comp Sci

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Among several methods used for calculating the root mean square (RMS) of electrical signals, Fourier and wavelet transforms are the most common approaches. The latter also has the advantage of being able to analyze both stationary and nonstationary signals. However, in the wavelet-based methods, the presence of both odd and even harmonics in the input signal causes the harmonic components not to be in the center of the extracted frequency bands and this will reduce the accuracy of the RMS calculation. In order to remove this drawback, this paper proposes a new method based on wavelet and Hilbert transforms, in which the frequency of all harmonic components is increased by half of the main frequency by using a preprocessing technique. In simulation results, the RMS value of a real signal of the steel electric arc furnace of the Esfahan Mobarakeh Steel Company is calculated by using the suggested method. The results clearly show that the accuracy of the proposed approach is better than that of conventional and grouping methods.

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Section: Wavelet Packet Transformmentioning

confidence: 99%

“…Applying a Hilbert transform to a signal twice in succession shifts the phases of all of the components by π rad. The Hilbert transform is defined by [22]:…”

Section: Hilbert Transformmentioning

confidence: 99%

A new combined method for RMS calculation based on wavelet packet and Hilbert transform

Hooshmand¹,

Modarresi²

2016

Turk J Elec Eng & Comp Sci

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“…The wavelet packet transform(WPT) has been used and a fuzzy wavelet packet power quality Index [7] has been developed and it is also redefined in time frequency domain under non stationary disturbance. This WPT [14] helps to measure the indices properly. For both fundamental and all harmonic signal a innovative method based wavelet packet transform has been used.…”

Section: Introductionmentioning

confidence: 99%

Power Quality Assessment of Perturbed Power System Implementing Fuzzy Logic and Discrete Wavelet Transform

Swain*,

Mishra,

Sinha

et al. 2019

IJITEE

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A novel power quality index (PQI) is determined in this paper which helps in determining the power quality under non-sinusoidal condition. Power quality monitoring is important due to exponential growth of non linear loads in electric power system. As non linear loads creates the distortion level in distribution system so it is highly necessary to measure power quality index. The innovative power quality index has been found out by considering three component such as Representative quality factor(RQPF), Detailed pollution factor(DPF), Total harmonic Distortion(THD). When total harmonic distortion of voltage(THDV) and Total harmonic distortion of current(THDI ) amalgamation occur then THD has been formed using fuzzy inference system. An experimental model has been developed to verify the PQI under different cases by measuring voltage and current both on the source side and utility side . The measured voltage and current are reformulated as wavelet function using discrete wavelet transform (DWT) to calculate referred power quality factors . This new power quality index has been validated through hardware model to justify its importance under different non-sinusoidal conditions.

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“…New power components have been defined to overcome the inability . Wavelet transform has been used to reformulate the newly defined power components . In , a new strategy has been proposed that is able to locate dominant harmonic source upstream or downstream from the metering section.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based technique for identification of harmonic source in distribution system

Sinha¹,

Goswami

Nath

2016

Int. Trans. Electr. Energ. Syst.

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SUMMARYA new method to identify the location and nature of the harmonic generating sources in a distribution system has been proposed. The method is based on wavelet decomposition of voltage and current signals at the point of measurement. Detail reactive power at level 1 of wavelet decomposition has been used. A harmonic generating load is identified by extracting its characterizing harmonics in the power system signals. The pseudo-frequency for the wavelet decomposition at level 1 is set at the characteristic frequency of the load, and the sampling frequency is so chosen that the desired harmonic information can be extracted at the detail level 1. Power systems signals are, however, captured at a higher sampling frequency. For the identification of the disturbing source, the required sampling frequency for level 1 decomposition is obtained using downsampling on the captured data. Applicability of the method has been demonstrated for both radial and non-radial power system networks.

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Power parameters calculations based on wavelet packet transform

Cited by 30 publications

References 10 publications

A new combined method for RMS calculation based on wavelet packet and Hilbert transform

A new combined method for RMS calculation based on wavelet packet and Hilbert transform

Power Quality Assessment of Perturbed Power System Implementing Fuzzy Logic and Discrete Wavelet Transform

Wavelet-based technique for identification of harmonic source in distribution system

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