Partial disharge recognition using artificial neural network

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 p...

Full description

Saved in:

Bibliographic Details
Format:	Thesis
Language:	English
Subjects:	Electric insulators and insulation Partial discharge (PD) Artificial neural network (ANN)
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/4/Nur%20Afifah.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-unimap-72711
record_format	uketd_dc
spelling	my-unimap-727112023-03-06T01:12:21Z Partial disharge recognition using artificial neural network Muzamir, Isa, Assoc. Prof. Dr. 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. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72711 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/1/Page%201-24.pdf 406a61a3d7bc2ddb7831e345bd025ae9 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/2/Full%20text.pdf 75c4301bad9614a934a178523acbada3 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/4/Nur%20Afifah.pdf d75f04866e54f0c93344abc89db2613e Universiti Malaysia Perlis (UniMAP) Electric insulators and insulation Partial discharge (PD) Artificial neural network (ANN) School of Electrical Systems Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Muzamir, Isa, Assoc. Prof. Dr.
topic	Electric insulators and insulation Partial discharge (PD) Artificial neural network (ANN)
spellingShingle	Electric insulators and insulation Partial discharge (PD) Artificial neural network (ANN) Partial disharge recognition using artificial neural network
description	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.
format	Thesis
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
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Electrical Systems Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72711/4/Nur%20Afifah.pdf
_version_	1776104227275276288

Partial disharge recognition using artificial neural network

Similar Items