Detection and Classification of Voltage Sag Causes Based on S-Transform and Extreme Learning Machine

Patnaik, Bhaskar; Panigrahi, Rasmi Ranjan; Mishra, Manohar; Jena, Ranjan Kumar; Swain, Manoj Kumar

doi:10.1007/978-981-15-2305-2_22

Cited by 5 publications

(4 citation statements)

References 9 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%

“…For instance, reference [81] presents a categorization according to different causes, namely, fault, self-extinguishing fault, line energizing, non-fault interruption, and transformer energizing. Similarly, voltage sags are usually classified according to the main underlying causes, i.e., faults, motor starting, and transformer energizing [175][176][177][178][179][180].…”

Section: Classificationmentioning

confidence: 99%

“…ANNs are typically used in classification problems where each neuron of the output layer represents a category and is activated according to the respective inputs. In the problem of multiple PQDs and voltage sag classification, several types of shallow ANNs have been used taking advantage of their flexibility and adaptability to problems where labels are well identified, for instance, the learning vector quantization [37][38][39], probabilistic neural network [44,52,56,65,71,84,95,102,123,147,168], self-organizing learning array [46], radial basis function [47,83], multilayer perceptron [48,53,57,65,89,93,102,168,177,178], adaptive linear network [82], feedforward [82,85,102], backpropagation [90], random vector functional link [113], and modular ANN [143]. Most recently, a learning algorithm known as ELM has been gaining popularity because of its remarkable efficiency.…”

Section: Shallow Artificial Neural Network (Anns)mentioning

confidence: 99%

“…ELM randomly chooses hidden nodes and analytically determines the output weights of a single-layer feedforward network [216]. Examples of ELM applications are [81,94,100,107,108,169,176,178].…”

Section: Shallow Artificial Neural Network (Anns)mentioning

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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