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Energies 2016, 9(7), 565; doi:10.3390/en9070565

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

1
Division de Ingenierias, Campus Irapuato-Salamanca, Universidad de Guanajuato/Carr. Salamanca-Valle km 3.5+1.8, Comunidad de Palo Blanco, Salamanca 36700, Guanajuato, Mexico
2
Facultad de Ingenieria, Universidad Autonoma de San Luis Potosi, Av. Manuel Nava 8, Zona Universitaria, San Luis Potosi 78290, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Eduardo Alonso
Received: 30 May 2016 / Revised: 4 July 2016 / Accepted: 12 July 2016 / Published: 20 July 2016
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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% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches. View Full-Text
Keywords: artificial neural networks; empirical mode decomposition; kurtosis; power quality disturbances; Shannon entropy; skewness artificial neural networks; empirical mode decomposition; kurtosis; power quality disturbances; Shannon entropy; skewness
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lopez-Ramirez, M.; Ledesma-Carrillo, L.; Cabal-Yepez, E.; Rodriguez-Donate, C.; Miranda-Vidales, H.; Garcia-Perez, A. EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments. Energies 2016, 9, 565.

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