Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review

Granados-Lieberman, David; Romero-Troncoso, René de Jesús; Osornio-Ríos, Roque Alfredo; García-Pérez, Arturo; Cabal-Yépez, Eduardo

doi:10.1049/iet-gtd.2010.0466

Cited by 198 publications

(102 citation statements)

References 107 publications

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“…for higher frequency is greater than that of lower frequency, that is, Δx a > Δx b . [ 1] [ ] [ 1] [ ] a a a a a a…”

Section: Teo and Desa For Frequency Variationmentioning

confidence: 99%

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An Applicability of Teager Energy Operator and Energy Separation Algorithm for Waveform Distortion Analysis : Harmonics, Inter-harmonics and Frequency Variation

Cho¹,

Hur²,

Chung³

2014

Journal of Electrical Engineering and Technology

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-This paper deals with an application of Teager Energy Operator (TEO) and Energy Separation Algorithm(ESA) to detect and determine various voltage waveform distortions like harmonics, inter-harmonics and frequency variation. Because the TEO and DESA algorithm was initially proposed for speech or communication analysis, its applications are limited to some types of waveform in the power quality analysis area. For example, an undistorted voltage signal is similar with a pure sinusoid. A voltage fluctuation is very similar with an amplitude-modulated signal, from the viewpoint of signal theory. And a continuous frequency variation is similar with a frequencymodulated signal, which is also known as a chirp signal. This paper is written to show that the TEO and DESA algorithm can be used for detecting occurrences of the representative waveform distortions and determining their instantaneous information of amplitude and frequency.

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“…for higher frequency is greater than that of lower frequency, that is, Δx a > Δx b . [ 1] [ ] [ 1] [ ] a a a a a a…”

Section: Teo and Desa For Frequency Variationmentioning

confidence: 99%

“…Reference [1] provides a useful and general review on various methodologies used for the power quality analysis and the event classification. However, the calculation burdens will cause a relatively slow response, since most of them are implemented in the computer based processors.…”

Section: Introductionmentioning

confidence: 99%

An Applicability of Teager Energy Operator and Energy Separation Algorithm for Waveform Distortion Analysis : Harmonics, Inter-harmonics and Frequency Variation

Cho¹,

Hur²,

Chung³

2014

Journal of Electrical Engineering and Technology

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“…GK bozulmaları, elektrik şebekesinin ekonomik işletilmesini olumsuz etkilemektedir [3]. Mevcut şebekelerin akıllı şebekelere dönüşmeye başladığı günümüzde, hem üreticilerin hem de tüketicilerin yüksek GK seviyesine ulaşmalarını sağlamak için otomatik olarak GK'nın izlenmesi, bozulmaların tanınması ve sınıflandırılması önemli olmaktadır [3,4]. GK bozulmalarını ayırt etmek için her bir bozulmayı temsil edecek özellikler belirlenmelidir [4].…”

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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“…However, because of the sensitivity of noise and insufficient data, the accuracy of disturbance patterns may be affected. Given the complexity and various types of PQ disturbance signals, the application of artificial neural network (ANN) [10][11][12][13] and fuzzy logic [14][15][16][17][18][19][20][21][22] has gotten increasing attention. ANN, which has strong adaptivity and fault tolerance, is a powerful tool for recognizing PQ disturbances and has already obtained remarkable research results.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Liu¹,

Liu²,

Xu³

2016

Journal of Electrical Engineering and Technology

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-This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

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Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review

Cited by 198 publications

References 107 publications

An Applicability of Teager Energy Operator and Energy Separation Algorithm for Waveform Distortion Analysis : Harmonics, Inter-harmonics and Frequency Variation

An Applicability of Teager Energy Operator and Energy Separation Algorithm for Waveform Distortion Analysis : Harmonics, Inter-harmonics and Frequency Variation

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

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

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