An image processing based method for power quality event identification

Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul

doi:10.1016/j.ijepes.2012.10.049

Cited by 32 publications

(14 citation statements)

References 22 publications

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“…In [58], image processing techniques like gamma correction, edge detection, and peak and valley detection were proposed to PQ data represented as a transverse wave having compressions and rarefactions to identify the PQ disturbances. In [59], a 2D-DWT based image processing technique was proposed for classifying multiple PQ events.…”

Section: Miscellaneous Wavelet Transforms For Feature Extractionmentioning

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

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Section: Miscellaneous Wavelet Transforms For Feature Extractionmentioning

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

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“…Here, the time-characterised disturbances (such as sag and interruption) were classified by the rule-based method, and the frequency-characterised feature-based PQDs (such as transients and impulses) were categorized by using WPT-based HMM. Shareef et al 55 proposed a new approach to visualize and detect the PQ disturbances using image processing technique (IPT) like greyscale images and binary images. Initially, the disturbance signals were processed by WPT and decomposed into several components of different frequency sub-bands.…”

Section: Wavelet Transform and Its Variants' Based Methodsmentioning

confidence: 99%

“…Ece and Gerek 54 proposed a two-dimensional discrete wavelet transform (2DDWT) method for automatic recognition of PQ disturbances. Shareef et al 55 proposed a new approach to visualize and detect the PQ disturbances using image processing technique (IPT) like greyscale images and binary images. Similarly, a two-dimensional discrete wavelet transform-based IPT was proposed by Krishna and Kaliaperumal 56 for classification of PQ signals.…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

138

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Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

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“…Literatürde GK bozulmalarını temsil eden özniteliklerin çıkartılması aşamasında bir boyutlu sinyal işleme yöntemleri kullanılmaktadır. Bu çalışmanın da temelini oluşturan sinyal işleme yöntemi olarak 2B-ADD [10][11][12] çalışmaları mevcuttur. Ece ve Gerek yaptıkları çalışmada GK bozulma verisi üzerinde bir periyot boyunca değişim gösteren dalga şekillerini 2 boyutlu görüntü matrisine çevirmişlerdir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Güç Kalitesi Bozulmalarının 2 Boyutlu Ayrık Dalgacık Dönüşümü ve Torbalama Karar Ağaçları Yöntemi ile Sınıflandırılması

Karasu¹,

Saraç²

2018

Journal of Polytechnic

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An image processing based method for power quality event identification

Cited by 32 publications

References 22 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

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Güç Kalitesi Bozulmalarının 2 Boyutlu Ayrık Dalgacık Dönüşümü ve Torbalama Karar Ağaçları Yöntemi ile Sınıflandırılması

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