Design of modified Matched Wavelet Design using Lagrange interpolation

Saini, Manish Kumar; Beniwal, Rajender Kumar

doi:10.1109/cipech.2016.7918775

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…Accordingly, PQ event detection in this work has been progressed only with Db4 wavelet and Db4 fraclet. The performance of fraclet has also been evaluated on 1D and 2D signal …”

Section: Fundamentals Of Fractional Delay Filtermentioning

confidence: 99%

“…The performance of fraclet has also been evaluated on 1D and 2D signal. 17 For analyzing the stability of the proposed filter, pole-zero plots have also been presented. In the analysis of pole-zero plot in z-domain, the coefficients are delayed by a factor of 0.1 to 0.9, i.e., in fraction.…”

Section: Performance Analysis Of Designed Fractional Delay Filtermentioning

confidence: 99%

“…The primary scheme of the fraclet design is to fractionally delay these filter coefficients by using Lagrangian multiplier. The FD wavelets or fraclets have lower energy compaction ratio (ECR) than the corresponding UD wavelets, as investigated . The fractional delay, d , has value between 0 and 1 (0 < d < 1).…”

Section: Introductionmentioning

confidence: 99%

“…The FD wavelets or fraclets have lower energy compaction ratio (ECR) than the corresponding UD wavelets, as investigated. 17 The fractional delay, d, has value between 0 and 1 (0 < d < 1). Among the filter coefficients obtained for different delays, the fractional delay corresponding to the lowest ECR have been selected for generating filter coefficients as the optimal fraclet.…”

Section: Introductionmentioning

confidence: 99%

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Optimum fractionally delayed wavelet design for PQ event detection and classification

Saini

Beniwal

2017

Int Trans Electr Energ Syst

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Summary This work introduces an optimized wavelet designed by fractionally delaying the coefficients of the unit delay filter. The wavelets produced by optimized fractionally delayed filter coefficients are named as fraclets. Fraclet has been utilized in the real‐time power quality events' detection and classification which has been hitherto addressed with the help of unit delay filters. Normal signal, voltage sag, swell, harmonics, sag with harmonics, swell with harmonics, sag and swell with harmonics, and sag and swell with interruption are considered in this work to validate the performance of proposed algorithm based on fraclet. Along with real‐time generation of various power quality events with TMS320C6748 DSP board, different events have also been simulated by using parametric equations. The upper hand of fraclets over wavelets has been highlighted by maximally flat frequency response of fraclets. Moreover, fraclets facilitate better multiresolution analysis by providing low energy compaction ratio (ECR). Further, 11 characteristic features have also been extracted from each decomposition level of PQ signal up to fifth level and fed to the modified probabilistic neural network (MPNN) for validating the proposed power quality event detection and classification algorithm. MPNN has outperformed the support vector machine (SVM), both with fraclet and wavelet. The proposed algorithm relying on fraclets has shown better results as compared with wavelet.

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“…Accordingly, PQ event detection in this work has been progressed only with Db4 wavelet and Db4 fraclet. The performance of fraclet has also been evaluated on 1D and 2D signal …”

Section: Fundamentals Of Fractional Delay Filtermentioning

confidence: 99%

Section: Performance Analysis Of Designed Fractional Delay Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimum fractionally delayed wavelet design for PQ event detection and classification

Saini

Beniwal

2017

Int Trans Electr Energ Syst

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“…The process of designing the new coefficients of Coiflet fraclet with a delay, d , first involves the designing of Lagrange filter coefficients of order R and having delay, d and then the multiplication of Lagrange filter coefficients with the Coiflet wavelet coefficients. This filter introduces an approximate time delay ( D int + d ) T into the wavelet coefficients, where D int is an integer value, d is a fraction of number ( 0 < d < 1 ) and T is the sampling time (Saini and Beniwal, 2016).…”

Section: Fractionally Delayed Coiflet Wtmentioning

confidence: 99%

Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

Saini

Kapoor

Beniwal

et al. 2019

Transactions of the Institute of Measurement and Control

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In this work, a new fractionally delayed biorthogonal wavelet is designed for recognition of voltage sag causes. This work addresses the classification of voltage sag causes into three categories, i.e., fault events (namely line-to-ground fault, line-to-line fault, double-line-to-ground fault and symmetrical fault), induction motor starting and transformer energization. Fractionally delayed biorthogonal Coiflet wavelet of order 2 (named as Coiflet fraclet) is designed using Lagrange interpolation and employed for the multiresolution analysis of voltage sag signals. To achieve higher classification accuracy, event information is derived from both time-frequency domain and frequency domain. Frequency information is obtained by applying multiple signal classification (MUSIC) algorithm on voltage sag signals. Both the time-frequency domain and frequency domain features are concatenated to implement the feature level fusion for strengthening the feature set. The feature set obtained after feature level fusion is used for training the recursive reduced kernel based extreme learning machine which addresses the problem of highly complex structure due to huge dataset of large dimensions. The classifier has shown robustness to the presence of noise also and classified the voltage sag causes with high accuracy.

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Analysis of PQ Disturbances in Renewable Grid Integration System Using Non-parametric Spectral Estimation Approach

Beniwal¹,

Saini²

2020

Advances in Intelligent Systems and Computing

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Design of modified Matched Wavelet Design using Lagrange interpolation

Cited by 4 publications

References 7 publications

Optimum fractionally delayed wavelet design for PQ event detection and classification

Optimum fractionally delayed wavelet design for PQ event detection and classification

Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

Analysis of PQ Disturbances in Renewable Grid Integration System Using Non-parametric Spectral Estimation Approach

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