Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

Saini, Manish Kumar; Kapoor, Rajiv; Beniwal, Rajender Kumar; Aggarwal, Akanksha

doi:10.1177/0142331218814292

Cited by 10 publications

(7 citation statements)

References 57 publications

(59 reference statements)

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“…Discrete WT (DWT) is the discrete realization of WT and has been widely used for detection and classification of multiple PDQs as the main transformation technique [33, 34, 37-39, 42, 44-46, 48, 51, 53, 55, 59, 60, 62-64, 69, 76, 83, 95, 101, 110, 123] or combined with other techniques [35,36,41,61,73,80,102,117,126]. It has also been used as the main transformation technique for the detection and classification of voltage sags [133,142,146,154,168,169] and notches [187, 190, 191, 193, 194, 197-199, 204, 208], or combined with other techniques for voltage sags [160,181,186] and notches [189,192,196,202]. DWT is given by [33]:…”

Section: Time-frequency Domainmentioning

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: Time-frequency Domainmentioning

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

“…DWT and self organizing mapping neural network (SOMNN) are used by the authors for detection/classification of load and capacitor switching in [127]. Differences in orthogonal, bi-orthogonal and semi-orthogonal wavelets is presented in [128], from PQ view point and fractionally delayed biorthogonal wavelet in [129] . Multiwavelet transform based classification of PQ events is presented in [130], [131], whereas multiwavelet and Dampster-Shafer technique is proposed in [132], [133].…”

Section: B Wavelet Transformmentioning

confidence: 99%

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

et al. 2021

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Recently, power quality (PQ) issues have drawn considerable attention of the researchers due to the increasing awareness of the customers towards power quality. The PQ issues maintain its preeminence because of the significant growth encountered in the smart grid technology, distributed generation, usage of sensitive and power electronic equipments with the integration of renewable energy resources. The IoT and 5G networks technologies have a number of advantages like smart sensor interfacing, remote sensing and monitoring, data transmission at high speed. Due to this, applications of these two are highly adopted in smart grid. The prime focus of the paper is to present an exhaustive survey of detection and classification of power quality disturbances by discussing signal processing techniques and artificial intelligence tools with their respective pros and cons. Further, critical analysis of automatic recognition techniques for the concerned field is posited with the viewpoint of the types of power input signal (synthetic/real/noisy), pre-processing tools, feature selection methods, artificial intelligence techniques and modes of operation (online/offline) as per the reported articles. The present work also elaborates the future scope of the said field for the reader. This paper provides valuable guidelines to the researchers those having interest in the field of PQ analysis and exploring the better methodologies for further improvement. Comprehensive comparisons have been presented with the help of tabular presentations. Although this critical survey cannot be collectively exhaustive, still this survey comprises the most significant works in the concerned paradigm by examining more than 300 research publications.

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“…All these events cause a momentary increase in current that results in sag at point of common coupling (PCC). For detection of causes responsible for generating VS, several signal processing algorithms have been employed in the literature, for example, independent component analysis [10], empirical mode decomposition and Hilbert transform [11], discrete wavelet transform (DWT) [12][13][14], fractionally delayed wavelet transform [15,16], S-transform [17,18], wavelet transform (WT) with spectral and statistical analysis [19] and variational mode decomposition [20]. The domain of signal processing techniques employed for spectral analysis of PQ signals is being ruled by wavelets in one or another form because wavelets can capture all information of signal, for example, trends, breakdown points, discontinuities in higher derivatives and self-similarity [21], which cannot be revealed by other signal processing techniques proposed by other research works.…”

Section: Introductionmentioning

confidence: 99%

Lifting scheme‐based matched wavelet design for effective characterisation of different types of voltage sag

Aggarwal

Saini

2021

IET Science Measure & Tech

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This study presents a novel scheme for designing biorthogonal wavelets matched to the voltage sag (VS) signals. Instead of using standard wavelets, matched wavelets designed in this study can extract more distinguishing features for better characterisation of sag signals, although this study proposes only the designing process of matched wavelets. The key method used for designing matched wavelets is the lifting scheme, which has never been reported in the literature for constructing matched wavelets for PQ signals. Besides using the lifting scheme, the proposed work preprocesses sag signals to make input signal of lifting scheme rich of sag-related information. During pre-processing, 1-D sag signal is first converted to 2-D form for capturing information of every small transition (which may be overlooked in 1-D form) and then made 1-D by ring projection to implement 1-D lifting scheme. In this way, biorthogonal wavelets have been well matched to every type of VS signal collected through the experimental setup simulating different line faults, induction motor starting, transformer energisation and heavy load starting. Also, the characteristics of designed wavelets are evaluated in a MATLAB environment using frequency response and pole-zero mapping in z-plane. Higher PSNR values during signal reconstruction proved outperformance of newly designed matched wavelets.How to cite this article: Aggarwal A, Saini MK. Lifting scheme-based matched wavelet design for effective characterisation of different types of voltage sag.

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Recognition of voltage sag causes using fractionally delayed biorthogonal wavelet

Cited by 10 publications

References 57 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

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

Lifting scheme‐based matched wavelet design for effective characterisation of different types of voltage sag

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