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A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network

Automatic classification of Power Quality Disturbances (PQDs) is a challenging concern for both the utility and industry. In this paper, a novel technique of automatic classification of single and hybrid PQDs is proposed. The proposed algorithm consists of the Discrete Wavelet Transform (DWT) and Pr...

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Main Authors: Khokhar, Suhail, Mohd. Zin, Abdullah Asuhaimi, Momen, Aslam Pervez, Mokhtar, Ahmad Safawi
Format: Article
Published: Elsevier 2017
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/66455/
http://dx.doi.org/10.1016/j.measurement.2016.10.013
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spelling my.utm.664552017-10-03T07:59:05Z http://eprints.utm.my/id/eprint/66455/ A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network Khokhar, Suhail Mohd. Zin, Abdullah Asuhaimi Momen, Aslam Pervez Mokhtar, Ahmad Safawi TK Electrical engineering. Electronics Nuclear engineering Automatic classification of Power Quality Disturbances (PQDs) is a challenging concern for both the utility and industry. In this paper, a novel technique of automatic classification of single and hybrid PQDs is proposed. The proposed algorithm consists of the Discrete Wavelet Transform (DWT) and Probabilistic Neural Network based Artificial Bee Colony (PNN-ABC) optimal feature selection of PQDs. DWT with Multi-Resolution Analysis (MRA) is used for the feature extraction of the disturbances. The PNN classifier is used as an effective classifier for the classification of the PQDs. However, the two critical concerns such as the selection of the optimal features and the spread constant value might affect the performance of the classifier. Hence, these two issues are addressed using a novel technique PNN-ABC based optimal feature selection and parameter optimization for improving the performance of the classification system. The ABC algorithm is used to select optimal features from a large feature set and the optimal value of the PNN spread constant. The optimal feature selection method retains the useful features and discards the redundant features. The performance of the proposed algorithm is evaluated by PSCAD/EMTDC simulation of a typical 11 kV underground distribution system of Malaysia. The noise-riding PQDs have also been analysed to validate the sensitivity of the proposed algorithm. The simulation results show that the new PNN-ABC based optimal feature selection algorithm is proficient and accurate in classifying the PQDs. Elsevier 2017-01-01 Article PeerReviewed Khokhar, Suhail and Mohd. Zin, Abdullah Asuhaimi and Momen, Aslam Pervez and Mokhtar, Ahmad Safawi (2017) A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement, 95 . pp. 246-259. ISSN 0263-2241 http://dx.doi.org/10.1016/j.measurement.2016.10.013 DOI:10.1016/j.measurement.2016.10.013
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Khokhar, Suhail
Mohd. Zin, Abdullah Asuhaimi
Momen, Aslam Pervez
Mokhtar, Ahmad Safawi
A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network
description Automatic classification of Power Quality Disturbances (PQDs) is a challenging concern for both the utility and industry. In this paper, a novel technique of automatic classification of single and hybrid PQDs is proposed. The proposed algorithm consists of the Discrete Wavelet Transform (DWT) and Probabilistic Neural Network based Artificial Bee Colony (PNN-ABC) optimal feature selection of PQDs. DWT with Multi-Resolution Analysis (MRA) is used for the feature extraction of the disturbances. The PNN classifier is used as an effective classifier for the classification of the PQDs. However, the two critical concerns such as the selection of the optimal features and the spread constant value might affect the performance of the classifier. Hence, these two issues are addressed using a novel technique PNN-ABC based optimal feature selection and parameter optimization for improving the performance of the classification system. The ABC algorithm is used to select optimal features from a large feature set and the optimal value of the PNN spread constant. The optimal feature selection method retains the useful features and discards the redundant features. The performance of the proposed algorithm is evaluated by PSCAD/EMTDC simulation of a typical 11 kV underground distribution system of Malaysia. The noise-riding PQDs have also been analysed to validate the sensitivity of the proposed algorithm. The simulation results show that the new PNN-ABC based optimal feature selection algorithm is proficient and accurate in classifying the PQDs.
format Article
author Khokhar, Suhail
Mohd. Zin, Abdullah Asuhaimi
Momen, Aslam Pervez
Mokhtar, Ahmad Safawi
author_facet Khokhar, Suhail
Mohd. Zin, Abdullah Asuhaimi
Momen, Aslam Pervez
Mokhtar, Ahmad Safawi
author_sort Khokhar, Suhail
title A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network
title_short A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network
title_full A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network
title_fullStr A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network
title_full_unstemmed A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network
title_sort new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network
publisher Elsevier
publishDate 2017
url http://eprints.utm.my/id/eprint/66455/
http://dx.doi.org/10.1016/j.measurement.2016.10.013
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score 13.244199

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