EMD-based feature extraction for power quality disturbance classification using moments
type: Generically Dependent Continuant
related by: authorship_27157
type: Article
freetext keyword: Artificial neural networks; Empirical mode decomposition; Kurtosis; Power quality disturbances; Shannon entropy; Skewness Electric power systems; Higher order statistics; Neural networks; Signal processing; Signal to noise ratio; Empirical Mode Decomposition; Kurtosis; Power quality disturbances; Shannon entropy; Skewness; Power quality
type: Document
related by: authorship_27160
type: Entity
most specific type: Article
related by: authorship_27159
estado: published
type: Continuant
has publication venue: Energies
artnum: A565
related by: authorship_27162
doi: 10.3390/en9070565
accesstype: All Open Access, Gold, Green
type: Thing
e iscopus: 2-s2.0-84988884824
related by: authorship_27158
issue: 7
crisid: 4622
related by: authorship_27161
abstract: In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100%25 of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
volume: 9
type: Information Content Entity
origen: scopus
most specific type: Document
date time value: article date 4622

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RDF description of EMD-based feature extraction for power quality disturbance classification using moments - http://orbis.uaslp.mx/vivo/individual/scopus4621
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date: 2026-08-27T05:46:30
publisher: vivo
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Article
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Energies
type: Document
type: Periodical
type: Generically Dependent Continuant
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publication venue for: EMD-based feature extraction for power quality disturbance classification using moments
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