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Partial disharge recognition using artificial neural network

Master of Science in Electrical System Engineering

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Bibliographic Details
Main Author: Nur Afifah, Yusoff
Other Authors: Muzamir, Isa, Assoc. Prof. Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2017
Subjects:
Electric insulators and insulation
Partial discharge (PD)
Artificial neural network (ANN)
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72711
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spelling my.unimap-727112023-03-06T01:12:21Z Partial disharge recognition using artificial neural network Nur Afifah, Yusoff Muzamir, Isa, Assoc. Prof. Dr. Electric insulators and insulation Partial discharge (PD) Artificial neural network (ANN) Master of Science in Electrical System Engineering Partial discharge (PD) seriously affects the reliability of the distribution system due to electrical stress and the duration of the installation. Recent technology advance brings the analysis of the PD act as the guideline and maintenance strategy can be carried out when a parameter exceeding the predefined level. This thesis presents an artificial neural network (ANN) modeling in recognizing the PD signal. PD signals are generated from experimental measurement and simulation by using electromagnetic transient program-alternative transient program (EMTP-ATP). There are two analyses are carried out; classification and de-noising of PD signal. The first analysis is aim to discriminate between PD and noise signals. Multilayer perceptron with back propagation algorithm is used to perform this task. The result shows that the number of nodes in hidden layer affects the accuracy of classification. Second analysis presents the de-noising performance of PD signal using three different techniques; ANN, fast Fourier transforms (FFT) and discrete wavelet transform (DWT). The objective of this analysis is to yield the PD signal from the measured signal which is the combination of PD and noise signals. Only PD signals generated from EMTP-ATP simulation environment is considered. The de-noising algorithm is implemented to discover a clean PD signal from disrupted signal. The performance of the de-nosing techniques was evaluated by comparing the signal to noise ratio (SNR). In order to de-noise the disturbed PD signal, the knowledge of interference peak needs to take into account. The result of this analysis shows ANN is the best de-noising technique as all the other techniques produce a peak higher than PD signal peak. 2017 2021-11-05T07:43:24Z 2021-11-05T07:43:24Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72711 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Electrical Systems Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Electric insulators and insulation
Partial discharge (PD)
Artificial neural network (ANN)
spellingShingle Electric insulators and insulation
Partial discharge (PD)
Artificial neural network (ANN)
Nur Afifah, Yusoff
Partial disharge recognition using artificial neural network
description Master of Science in Electrical System Engineering
author2 Muzamir, Isa, Assoc. Prof. Dr.
author_facet Muzamir, Isa, Assoc. Prof. Dr.
Nur Afifah, Yusoff
format Thesis
author Nur Afifah, Yusoff
author_sort Nur Afifah, Yusoff
title Partial disharge recognition using artificial neural network
title_short Partial disharge recognition using artificial neural network
title_full Partial disharge recognition using artificial neural network
title_fullStr Partial disharge recognition using artificial neural network
title_full_unstemmed Partial disharge recognition using artificial neural network
title_sort partial disharge recognition using artificial neural network
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2017
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72711
_version_ 1772813062413221888
score 13.222552

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