2019
DOI: 10.3390/app9183681
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Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network

Abstract: This paper presents a hybrid approach combining phase space reconstruction (PSR) with a convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a PSR technique is developed to transform a 1D voltage disturbance signal into a 2D image file. Then, a CNN model is developed for the image classification. The feature maps are extracted automatically from the image file and different patterns are derived from variables in CNN. A set of synthetic signals, as well as operational … Show more

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Cited by 26 publications
(14 citation statements)
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“…Balouji and Salor [17] phase space reconstruction (PSR) and convolutional neural networks (CNN) are used in a hybrid technique for PQ event classification that processes 2D image files representing voltage disturbances demonstrating more accurate results compared with existing methods. A complete framework for PQ event classification using 2D CNNs and space phasor model (SPM) is presented in [18]. In the proposed approach in order to reduce the complexity and improve efficiency in PQ event classification the input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands.…”
Section: Introductionmentioning
confidence: 99%
“…Balouji and Salor [17] phase space reconstruction (PSR) and convolutional neural networks (CNN) are used in a hybrid technique for PQ event classification that processes 2D image files representing voltage disturbances demonstrating more accurate results compared with existing methods. A complete framework for PQ event classification using 2D CNNs and space phasor model (SPM) is presented in [18]. In the proposed approach in order to reduce the complexity and improve efficiency in PQ event classification the input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands.…”
Section: Introductionmentioning
confidence: 99%
“…The PQEs (voltage sag, swell, harmonics, transients, interruptions, unbalanced voltage and current, etc.) can influence the overall performance of MG operation and shorten the life time of power devices integrated into the MG system [ 2 , 3 ]. Therefore, to achieve improved PQ with safe and reliable operation of the MG power system, the source of PQEs must be detected and classified by means of applying advanced classification techniques [ 3 , 4 ].…”
Section: Introductionmentioning
confidence: 99%
“…Autoencoder, convolutional neural networks, or recurrent neural networks are the most common techniques to be used for dealing with complex data involved. There is merely a few works exploring the suitability of such techniques in electrical network monitoring, but even less applied to PQD classification [22]. The work presented in [23] submits data-driven approaches on the grounds of such deep learning techniques.…”
Section: Introductionmentioning
confidence: 99%