Advanced signal processing and machine learning techniques for voltage sag causes detection in an electric power system

Mishra, Manohar; Panigrahi, Rasmi Ranjan

doi:10.1002/2050-7038.12167

Cited by 16 publications

(9 citation statements)

References 30 publications

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“…(2) [187,[189][190][191][192][193][194][196][197][198][199] ST has been used for the detection and classification of multiple PQDs as the main transformation technique [40, 43, 49, 52, 54, 57, 68, 70-72, 75, 77, 78, 81, 85, 92, 94, 96, 104, 105, 118, 120, 125] or in combination with a spline wavelet [41], TT [65,109,114], VMD [88], WT [117], and others [102]. Similarly, it has been used for the assessment of sags as the main technique [143,149,178] or combined with VMD [176] and FT [184].…”

Section: Time-frequency Domainmentioning

confidence: 99%

“…Transformation techniques widely used in other domains have been adopted by researchers for the detection and classification of PQDs. The MD technique is mainly composed of EMD [86,93,129] and VMD [88,99,100,124,130] for the assessment of multiple PQDs, and also for the specific assessment of voltage sags [176]. EMD takes the linear or non-linear input signal and iteratively decomposes it into a series of smaller components known as Intrinsic Mode Functions (IMF).…”

Section: Miscellaneousmentioning

confidence: 99%

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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%

Section: Miscellaneousmentioning

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

“…Real measured data lacks class labels, supervised learning cannot be carried out bases on the data. Therefore, according to the simulation method proposed in reference [25], this article builds the voltage sag simulation system model as shown in Figure 2 under the Simulink environment. It represents the simulation system models of three voltage sag sources: short-circuit fault, transformer energizing and induction motor starting respectively.…”

Section: A Simulation Model Establishment and Data Generationmentioning

confidence: 99%

Classification Method of Voltage Sag Sources Based on Sequential Trajectory Feature Learning Algorithm

Zhang

et al. 2022

IEEE Access

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The classification of voltage sag sources is essential for the establishment of controlling scheme and reasonable division of responsibilities in voltage sag-associated accidents. Existing methods for classifying voltage sag sources usually ignore the interpretability of the classification model, and are only dedicated to improving the accuracy of voltage classification, which cannot provide a reliable classification basis for users and power enterprises. Therefore, this article proposes an effective and interpretable voltage sag sources classification method based on sequential trajectory feature learning and Random Forest algorithm. Firstly, to fully consider the interpretable sequential trajectory features of voltage sag signals so as to improve the quality and interpretability of calculation process, the fused lasso generalized eigenvector (FLAG) algorithm is adopted to quickly search for interpretable shapelets sub-sequences from the labeled voltage sag data. After that, the labeled data and samples to-be-classified are subjected to shapelet transformation through the shapelet sub-sequences to obtain the sequential trajectory features. Finally, the random forest is trained on the labeled sequential trajectory feature data to achieve supervised sample classification, which inherits the interpretability of shapelet. To test the feasibility and validity of the proposed voltage sag sources classification method, the simulation cases based on the simulated voltage sag signals were studied. The simulation results show that the proposed method has significant advantages in terms of accuracy and interpretability of voltage sag sources classification.

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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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Advanced signal processing and machine learning techniques for voltage sag causes detection in an electric power system

Cited by 16 publications

References 30 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

Classification Method of Voltage Sag Sources Based on Sequential Trajectory Feature Learning Algorithm

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

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