Staff View: Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine

Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine

Discrete wavelet transforms; Electrolysis; Power quality; Signal reconstruction; Border distortion; Discrete-wavelet-transform; Distortion effects; Sliding Window; Support vector machine classification; Support vectors machine; Transient disturbances; Transient overvoltages; Transient signal; Window...

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Main Authors:	Asman S.H., Abidin A.F., Yusoh M.A.T.M., Subiyanto S.
Other Authors:	57194493395
Format:	Article
Published:	Elsevier B.V. 2023
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id	my.uniten.dspace-26958
record_format	dspace
spelling	my.uniten.dspace-269582023-05-29T17:38:09Z Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine Asman S.H. Abidin A.F. Yusoh M.A.T.M. Subiyanto S. 57194493395 26666522700 56453466100 57224199114 Discrete wavelet transforms; Electrolysis; Power quality; Signal reconstruction; Border distortion; Discrete-wavelet-transform; Distortion effects; Sliding Window; Support vector machine classification; Support vectors machine; Transient disturbances; Transient overvoltages; Transient signal; Window techniques; Support vector machines The existing border distortion effect at signal edges can produce inaccurate detection of transient signals when deploying signal processing method. Therefore, there is a need to develop a technique to minimise this border distortion effect through the use of Discrete Wavelet Transform (DWT). In this study, the extension mode has been proposed to minimise border distortion effect. DWT based on one-cycle window technique is used to extract the features of transient disturbances signal. The disturbances contain imprecision of data and provide insufficient information, thereby leading to the failure of the conventional method to identify any power quality (PQ) problems. Thus, the detection and classification method using Support Vector Machine (SVM) is deployed to acquire reliable and accurate classification technique. The novel approached of one-cycle sliding window with the association of extension mode are validated through the SVM classification. From the results obtained, the performance of absolute reconstructed signal after threshold technique shows that smooth padding is the most effective extension mode to reduce the border distortion effect using one-cycle sliding window. Overall, the SVM classification performance based on one-versus-one (OVO) coding design can detect transient and non-transient events subsequent to undergoing all subsequent processes. � 2021 The Authors Final 2023-05-29T09:38:09Z 2023-05-29T09:38:09Z 2022 Article 10.1016/j.rineng.2021.100311 2-s2.0-85121208348 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121208348&doi=10.1016%2fj.rineng.2021.100311&partnerID=40&md5=5ecaa2761272c0a76fc51ca32f735321 https://irepository.uniten.edu.my/handle/123456789/26958 13 100311 All Open Access, Gold Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Discrete wavelet transforms; Electrolysis; Power quality; Signal reconstruction; Border distortion; Discrete-wavelet-transform; Distortion effects; Sliding Window; Support vector machine classification; Support vectors machine; Transient disturbances; Transient overvoltages; Transient signal; Window techniques; Support vector machines
author2	57194493395
author_facet	57194493395 Asman S.H. Abidin A.F. Yusoh M.A.T.M. Subiyanto S.
format	Article
author	Asman S.H. Abidin A.F. Yusoh M.A.T.M. Subiyanto S.
spellingShingle	Asman S.H. Abidin A.F. Yusoh M.A.T.M. Subiyanto S. Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine
author_sort	Asman S.H.
title	Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine
title_short	Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine
title_full	Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine
title_fullStr	Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine
title_full_unstemmed	Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine
title_sort	identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine
publisher	Elsevier B.V.
publishDate	2023
_version_	1806428505748013056
score	13.223943

Identification of transient overvoltage using discrete wavelet transform with minimised border distortion effect and support vector machine

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