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Optimal feature selection using modified cuckoo search for classification of power quality disturbances

The widespread usages of sensitive equipment such as computers, controllers and microelectronic devices have placed immense burden on the grid operators to deliver high quality electrical power to their customers. To achieve this end, the power quality disturbances (PQD) within the network need to b...

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Main Authors:	Mehedi, Ibrahim Mustafa, Ahmadipour, Masoud, Salam, Zainal, Mohammed Ridha, Hussein, Bassi, Hussein, Rawa, Muhyaddin Jamal Hosin, Ajour, Mohammad, Abusorrah, Abdullah, Abdullah, Md. Pauzi
Format:	Article
Published:	Elsevier B.V. 2021
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/95404/ http://dx.doi.org/10.1016/j.asoc.2021.107897
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spelling	my.utm.954042022-05-31T12:37:44Z http://eprints.utm.my/id/eprint/95404/ Optimal feature selection using modified cuckoo search for classification of power quality disturbances Mehedi, Ibrahim Mustafa Ahmadipour, Masoud Salam, Zainal Mohammed Ridha, Hussein Bassi, Hussein Rawa, Muhyaddin Jamal Hosin Ajour, Mohammad Abusorrah, Abdullah Abdullah, Md. Pauzi TK Electrical engineering. Electronics Nuclear engineering The widespread usages of sensitive equipment such as computers, controllers and microelectronic devices have placed immense burden on the grid operators to deliver high quality electrical power to their customers. To achieve this end, the power quality disturbances (PQD) within the network need to be minimized. In this paper, a method to enhance the performance of the multiclass support vector machine (MSVM) classifier using the modified cuckoo search (MCS) is proposed. The wavelet packet transform is used to extract the crucial features from the PQD waveforms; these features are utilized as the input data to the classifier. In order to achieve high accuracy, robustness and speed, the MCS optimizes the number of selected features, as well as the penalty factor and slack variable of the MSVM. The proposed combinatorial algorithm (MCS-MSVM) is tested using 31 categories of PQD events; the hypothetical data for these events are generated by the IEEE 1159 Standard parametric equations. From simulation, the classification accuracy is recorded to be 100% under the no-noise condition, while at the signal-to-noise ratios (SNR) of 10, 20, 30 and 40 dB, the accuracies are 98.40, 98.54, 99.14 and 99.64%, respectively. Moreover, the comparative assessment concludes that the proposed method is superior to other heuristics-MSVM classification methods, namely the GA, PSO, differential evolution, harmony search and the conventional cuckoo search. The practical performance of the MCS-MSVM classifier is validated using real-time PQD data of a typical 11-kV underground distribution network, obtained from a particular electrical utility operator. For benchmarking, comparisons are made to 17 most recent PQD classification techniques published in literature. It is found that the proposed method exhibits the highest accuracies and the lowest computation times under ideal and noisy environments. Elsevier B.V. 2021-12 Article PeerReviewed Mehedi, Ibrahim Mustafa and Ahmadipour, Masoud and Salam, Zainal and Mohammed Ridha, Hussein and Bassi, Hussein and Rawa, Muhyaddin Jamal Hosin and Ajour, Mohammad and Abusorrah, Abdullah and Abdullah, Md. Pauzi (2021) Optimal feature selection using modified cuckoo search for classification of power quality disturbances. Applied Soft Computing, 113 . ISSN 1568-4946 http://dx.doi.org/10.1016/j.asoc.2021.107897 DOI:10.1016/j.asoc.2021.107897
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 Mehedi, Ibrahim Mustafa Ahmadipour, Masoud Salam, Zainal Mohammed Ridha, Hussein Bassi, Hussein Rawa, Muhyaddin Jamal Hosin Ajour, Mohammad Abusorrah, Abdullah Abdullah, Md. Pauzi Optimal feature selection using modified cuckoo search for classification of power quality disturbances
description	The widespread usages of sensitive equipment such as computers, controllers and microelectronic devices have placed immense burden on the grid operators to deliver high quality electrical power to their customers. To achieve this end, the power quality disturbances (PQD) within the network need to be minimized. In this paper, a method to enhance the performance of the multiclass support vector machine (MSVM) classifier using the modified cuckoo search (MCS) is proposed. The wavelet packet transform is used to extract the crucial features from the PQD waveforms; these features are utilized as the input data to the classifier. In order to achieve high accuracy, robustness and speed, the MCS optimizes the number of selected features, as well as the penalty factor and slack variable of the MSVM. The proposed combinatorial algorithm (MCS-MSVM) is tested using 31 categories of PQD events; the hypothetical data for these events are generated by the IEEE 1159 Standard parametric equations. From simulation, the classification accuracy is recorded to be 100% under the no-noise condition, while at the signal-to-noise ratios (SNR) of 10, 20, 30 and 40 dB, the accuracies are 98.40, 98.54, 99.14 and 99.64%, respectively. Moreover, the comparative assessment concludes that the proposed method is superior to other heuristics-MSVM classification methods, namely the GA, PSO, differential evolution, harmony search and the conventional cuckoo search. The practical performance of the MCS-MSVM classifier is validated using real-time PQD data of a typical 11-kV underground distribution network, obtained from a particular electrical utility operator. For benchmarking, comparisons are made to 17 most recent PQD classification techniques published in literature. It is found that the proposed method exhibits the highest accuracies and the lowest computation times under ideal and noisy environments.
format	Article
author	Mehedi, Ibrahim Mustafa Ahmadipour, Masoud Salam, Zainal Mohammed Ridha, Hussein Bassi, Hussein Rawa, Muhyaddin Jamal Hosin Ajour, Mohammad Abusorrah, Abdullah Abdullah, Md. Pauzi
author_facet	Mehedi, Ibrahim Mustafa Ahmadipour, Masoud Salam, Zainal Mohammed Ridha, Hussein Bassi, Hussein Rawa, Muhyaddin Jamal Hosin Ajour, Mohammad Abusorrah, Abdullah Abdullah, Md. Pauzi
author_sort	Mehedi, Ibrahim Mustafa
title	Optimal feature selection using modified cuckoo search for classification of power quality disturbances
title_short	Optimal feature selection using modified cuckoo search for classification of power quality disturbances
title_full	Optimal feature selection using modified cuckoo search for classification of power quality disturbances
title_fullStr	Optimal feature selection using modified cuckoo search for classification of power quality disturbances
title_full_unstemmed	Optimal feature selection using modified cuckoo search for classification of power quality disturbances
title_sort	optimal feature selection using modified cuckoo search for classification of power quality disturbances
publisher	Elsevier B.V.
publishDate	2021
url	http://eprints.utm.my/id/eprint/95404/ http://dx.doi.org/10.1016/j.asoc.2021.107897
_version_	1735386799967043584
score	13.211869

Optimal feature selection using modified cuckoo search for classification of power quality disturbances

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