A novel wavelet transform based voltage sag/swell detection algorithm

Latran, Mohammad Barghi; Teke, Ahmet

doi:10.1016/j.ijepes.2015.02.040

Cited by 76 publications

(36 citation statements)

References 34 publications

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“…It was proposed that various types of faults can be classified by using the less number of wavelet coefficients. Recently, a hybrid DWT based voltage sag/swell detection algorithm was proposed in [36] consisting of db2 and db8 mother wavelets to detect sag and swell with and without phase jumps. Refs.…”

Section: Wavelet Transform For Disturbances Detectionmentioning

confidence: 99%

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A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

228

111

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Section: Wavelet Transform For Disturbances Detectionmentioning

confidence: 99%

“…Refs. [19,[34][35][36] proposed the WT only for the detection of PQ disturbance signals. However, none of them have used WT for the feature extraction from PQ signals.…”

Section: Wavelet Transform For Disturbances Detectionmentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

228

111

View full text Add to dashboard Cite

“…The methodology is able to classify voltage sags/swells, harmonic distortions and interruptions. In [26], a sag/swell detection algorithm is proposed based on WT. The algorithm is the hybrid combination of Daubechies wavelets of order 2 and order 8.…”

Section: Introductionmentioning

confidence: 99%

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

et al. 2016

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Abstract:In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

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“…[23]. FFT algoritmasında, gerilim değişimlerini süzmek amacı ile gerilimin etkin değeri (rms değeri) kullanılmaktadır [24]. Bu yöntemde, son bir periyot verileri kullanılarak gerilimin etkin değeri hesaplanmaktadır.…”

Section: Si̇stem Yapisi (The System Structure)unclassified

Di̇nami̇k Geri̇li̇m İyi̇leşti̇ri̇ci̇lerde Geri̇li̇m Problemleri̇ni̇n Tespi̇ti̇ İçi̇n Yeni̇ Yöntem Geli̇şti̇ri̇lmesi̇

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2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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A novel wavelet transform based voltage sag/swell detection algorithm

Cited by 76 publications

References 34 publications

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

Di̇nami̇k Geri̇li̇m İyi̇leşti̇ri̇ci̇lerde Geri̇li̇m Problemleri̇ni̇n Tespi̇ti̇ İçi̇n Yeni̇ Yöntem Geli̇şti̇ri̇lmesi̇

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