Subcycle Voltage Dip Classification Using Matrix Pencil Method With Ellipse Fitting Algorithm

Chia, Meng Hwee; Khambadkone, A.M.

doi:10.1109/tia.2014.2347455

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…The characterization of voltage sags, i.e., the quantification of the parameters such as duration, magnitude, starting and ending phase angle, etc., is also performed in some studies [132,173,174]. Finally, this set of articles also includes research on the detection of sags for applications such as DVR and protection systems operation [134,136,153].…”

Section: Literature Reviewmentioning

confidence: 99%

A systematic review of real-time detection and classification of power quality disturbances

Navarro

Agudelo-Martínez²,

Trujillo

et al. 2023

Prot Control Mod Power Syst

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This paper offers a systematic literature review of real-time detection and classification of Power Quality Disturbances (PQDs). A particular focus is given to voltage sags and notches, as voltage sags cause huge economic losses while research on voltage notches is still very incipient. A systematic method based on scientometrics, text similarity and the analytic hierarchy process is proposed to structure the review and select the most relevant literature. A bibliometric analysis is then performed on the bibliographic data of the literature to identify relevant statistics such as the evolution of publications over time, top publishing countries, and the distribution by relevant topics. A set of articles is subsequently selected to be critically analyzed. The critical review is structured in steps for real-time detection and classification of PQDs, namely, input data preparation, preprocessing, transformation, feature extraction, feature selection, detection, classification, and characterization. Aspects associated with the type of disturbance(s) addressed in the literature are also explored throughout the review, including the perspectives of those studies aimed at multiple PQDs, or specifically focused on voltage sags or voltage notches. The real-time performance of the reviewed tools is also examined. Finally, unsolved issues are discussed, and prospects are highlighted.

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Section: Literature Reviewmentioning

confidence: 99%

A systematic review of real-time detection and classification of power quality disturbances

Navarro

Agudelo-Martínez²,

Trujillo

et al. 2023

Prot Control Mod Power Syst

View full text Add to dashboard Cite

show abstract

“…1 and the variables in whole range. [18][19][20] ★ ▀ ▪ A to G three-phase three-angle algorithm [21] ★► □ C and D three-phase-angle algorithm [22] ★► ▀ ○ C and D potential gradient method [23] ★► ▀ ▪ B, C and D two methods based on deep learning [24,25] ▴in [24] ★ in [25] Δ ▋ A,C and D SVA [26] ▴ ▪○ A to G, H and I five methods in [16,[27][28][29][30] which develop SVA in the field of dip type ▴ ▪○▋ A to G in [27] A to G, H and I in [16,[28][29][30] Input: ★ three-phase RMS voltage; ► three-phase angle jump; ▴ voltage waveform; pros and cons: ▪ sensitive/□ insensitive to greater PAJ or shallow dip; ◆ sensitive/◇ insensitive to PN-factor; ○ sensitive to threshold; ▀ straight/▐ complex; Δ higher accuracy. Take C a as example.…”

Section: Generate Vector Databasementioning

confidence: 99%

“…At present, many voltage dip/swell type calculation algorithms are published [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Table 1 summarises the characteristics, pros and cons of existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

New method for calculating voltage dip/swell types based on six‐dimensional vectors and Euclidean distance

Wang

Xiao

et al. 2019

IET Generation, Transmission & Distribution

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“…for obtaining the dip type from the measurement waveform,) are based on three main solutions: advanced feature extraction; RMS voltage; and complex voltages. In the feature extraction methods, signal processing tools like Support Vector Machine (SVM) [8], Fourier transform [9], wavelet transform [10], etc., are employed to extract useful features from the voltage-dip data. Based on the parameters and thresholds defined by experts, classification of voltage dips is achieved.…”

Section: Introductionmentioning

confidence: 99%

A LSTM-based deep learning method with application to voltage dip classification

Balouji

Bollen

et al. 2018

2018 18th International Conference on Harmonics and Quality of Power (ICHQP)

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In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent Neural Networks (RNNs). A total of 5982 three-phase one-cycle voltage dip RMS sequences, measured from several countries, has been used in our experiments. Our results have shown that the proposed method is able to classify the voltage dips from learned features in LSTM, with 93.40% classification accuracy on the test data set. The developed architecture is shown to be novel for feature learning and classification of voltage dips. Different from the conventional machine learning methods, the proposed method is able to learn dip features without requiring transition-event segmentation, selecting thresholds, and using expert rules or human expert knowledge, when a large amount of measurement data is available. This opens a new possibility of exploiting deep learning technology for power quality data analytics and classification.

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Subcycle Voltage Dip Classification Using Matrix Pencil Method With Ellipse Fitting Algorithm

Cited by 16 publications

References 15 publications

A systematic review of real-time detection and classification of power quality disturbances

A systematic review of real-time detection and classification of power quality disturbances

New method for calculating voltage dip/swell types based on six‐dimensional vectors and Euclidean distance

A LSTM-based deep learning method with application to voltage dip classification

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