Evaluating windowing-based continuous S-transform with neural network classifier for detecting and classifying power quality disturbances
The aim of this paper is to evaluate the implementation of windowing-based Continuous S-Transform (CST) techniques, namely, one-cycle and half-cycle windowing with Multi-layer Perception (MLP) Neural Network classifier. Both, the techniques and classifier are used to detect and classify the Power Qu...
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| Main Authors: | , , , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2019-03-01.
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| Online Access: | Get fulltext |
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| LEADER | 02449 am a22003493u 4500 | ||
|---|---|---|---|
| 001 | ijeecs16993_10856 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Daud, K. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Abidin, A. Farid |e author |
| 700 | 1 | 0 | |a Ismail, A. Paud |e author |
| 700 | 1 | 0 | |a Hasan, M. Daud A. |e author |
| 700 | 1 | 0 | |a Shafie, M. Affandi |e author |
| 700 | 1 | 0 | |a Ismail, A. |e author |
| 245 | 0 | 0 | |a Evaluating windowing-based continuous S-transform with neural network classifier for detecting and classifying power quality disturbances |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16993 | ||
| 520 | |a The aim of this paper is to evaluate the implementation of windowing-based Continuous S-Transform (CST) techniques, namely, one-cycle and half-cycle windowing with Multi-layer Perception (MLP) Neural Network classifier. Both, the techniques and classifier are used to detect and classify the Power Quality Disturbances (PQDs) into one of possible classes, voltage sag, swell and interrupt disturbance signal. For realizing evaluation, we proposed the methodology that include the PQD generation, the signal detection using windowing-based CST, the features extraction from S-contour matrices, PQD classification using MLP classifier. Then, we perform two type of assessments. Firstly, the accuracy assessment of chosen classifier in relation to three different training algorithms. Secondly, the execution time comparison of the training algorithms. Based on assessment results, we outline several recommendations for future work. | ||
| 540 | |a Copyright (c) 2018 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Power Quality, Power Quality Disturbance, Continuous S-Transform, Windowing Technique, Multi Layer Perception Neural Network | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 13, No 3: March 2019; 1136-1142 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v13.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16993/10856 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16993/10856 |z Get fulltext |