Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique

The quality of power supplies has become one of the issues concern of power supply utilities and users these days. Power quality (PQ) can bring severe problems such as processing interruption within the industries, malfunction and downtime of the equipment and impact in economic losses. Voltage vari...

Full description

Saved in:

Bibliographic Details
Main Author:	Tee, Wei Hown
Format:	Thesis
Language:	English English
Published:	2019
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utem.edu.my/id/eprint/24695/1/Voltage%20Variation%20Signals%20Source%20Identification%20System%20By%20Time-Frequency%20Analysis%20Technique.pdf http://eprints.utem.edu.my/id/eprint/24695/2/Voltage%20Variation%20Signals%20Source%20Identification%20System%20By%20Time-Frequency%20Analysis%20Technique.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.24695
record_format	uketd_dc
spelling	my-utem-ep.246952021-10-05T10:22:19Z Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique 2019 Tee, Wei Hown TK Electrical engineering. Electronics Nuclear engineering The quality of power supplies has become one of the issues concern of power supply utilities and users these days. Power quality (PQ) can bring severe problems such as processing interruption within the industries, malfunction and downtime of the equipment and impact in economic losses. Voltage variation signals are the common events in power systems that will affect the equipment or load within the premise. Thus, the source identification of the voltage variation is needed to reduce the impacts caused by the variation. This research presented the analysis and detection of voltage variation signals with time-frequency distributions (TFDs) which included spectrogram, Gabor transform and S-Transform. The voltage variation signals were generated in MATLAB/Simulink according to IEEE Standard 1159-2009. Parameters of the signals were calculated from the time-frequency representations (TFRs) and used for detection of the signals. The detection of the voltage variation signals were performed by k-Nearest Neighbors (kNN), Support Vector Machine (SVM) and rule-based classifiers in which both kNN and SVM gave 100% successful signals detection while rule-based gave successful detection above 97%. Based on the analysis, the best TFD was distinguished by comparing the performance analysis of the TFDs in terms of accuracy, memory size used and computational complexity of the signal analysis. Result showed that S-Transform was the best TFD to be used to analyze voltage variation signals among the three TFDs. In the source identification analysis, voltage variation signals from upstream, downstream and both uptream and downstream were simulated and analyzed by phase TFR. The phase angle of voltage and current variation signals calculated were similar to the input angle with precision above 0.95. The average impedance TFR phase power of the signals were calculated from the phase TFR of each variation signal at each problem source. The classification accuracy of source identification was performed by kNN, SVM and rule-based classifiers. SVM showed the best performance of 94.22% overall classification accuracy followed by kNN of 93.56% while rule-based showed the worst performance among the three classifiers with overall classification accuracy of 83.44%. 2019 Thesis http://eprints.utem.edu.my/id/eprint/24695/ http://eprints.utem.edu.my/id/eprint/24695/1/Voltage%20Variation%20Signals%20Source%20Identification%20System%20By%20Time-Frequency%20Analysis%20Technique.pdf text en public http://eprints.utem.edu.my/id/eprint/24695/2/Voltage%20Variation%20Signals%20Source%20Identification%20System%20By%20Time-Frequency%20Analysis%20Technique.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=116938 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electrical Engineering Yusoff, Mohd. Rahimi 1. Tee, W., Yusoff, M.R., Abdullah, A.R. and Yaakub, M.F., 2019. Voltage Variation Signals Source Identification and Diagnosis Method. International Journal of Advanced Computer Science and Applications, 10(4). (SCOPUS and WOS) 2. Tee, W., Yusoff, M.R., Abdullah, A.R. Jopri, M.H., Anwar, N.S.N., and Musa, H., 2019. Spectrogram Based Window Selection for the Detection of Voltage Variation. International Journal of Integrated Engineering, 11(3). (WOS) 3. Tee, W., Abdullah, A.R., Sutikno, T., Jopri, M.H. and Manap, M., 2018. A Comparative Modeling and Analysis of Voltage Variation by Using Spectrogram. system, 8, p.12. (SCOPUS)
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Yusoff, Mohd. Rahimi
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Tee, Wei Hown Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique
description	The quality of power supplies has become one of the issues concern of power supply utilities and users these days. Power quality (PQ) can bring severe problems such as processing interruption within the industries, malfunction and downtime of the equipment and impact in economic losses. Voltage variation signals are the common events in power systems that will affect the equipment or load within the premise. Thus, the source identification of the voltage variation is needed to reduce the impacts caused by the variation. This research presented the analysis and detection of voltage variation signals with time-frequency distributions (TFDs) which included spectrogram, Gabor transform and S-Transform. The voltage variation signals were generated in MATLAB/Simulink according to IEEE Standard 1159-2009. Parameters of the signals were calculated from the time-frequency representations (TFRs) and used for detection of the signals. The detection of the voltage variation signals were performed by k-Nearest Neighbors (kNN), Support Vector Machine (SVM) and rule-based classifiers in which both kNN and SVM gave 100% successful signals detection while rule-based gave successful detection above 97%. Based on the analysis, the best TFD was distinguished by comparing the performance analysis of the TFDs in terms of accuracy, memory size used and computational complexity of the signal analysis. Result showed that S-Transform was the best TFD to be used to analyze voltage variation signals among the three TFDs. In the source identification analysis, voltage variation signals from upstream, downstream and both uptream and downstream were simulated and analyzed by phase TFR. The phase angle of voltage and current variation signals calculated were similar to the input angle with precision above 0.95. The average impedance TFR phase power of the signals were calculated from the phase TFR of each variation signal at each problem source. The classification accuracy of source identification was performed by kNN, SVM and rule-based classifiers. SVM showed the best performance of 94.22% overall classification accuracy followed by kNN of 93.56% while rule-based showed the worst performance among the three classifiers with overall classification accuracy of 83.44%.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Tee, Wei Hown
author_facet	Tee, Wei Hown
author_sort	Tee, Wei Hown
title	Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique
title_short	Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique
title_full	Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique
title_fullStr	Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique
title_full_unstemmed	Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique
title_sort	voltage variation signals source identification system by time-frequency analysis technique
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electrical Engineering
publishDate	2019
url	http://eprints.utem.edu.my/id/eprint/24695/1/Voltage%20Variation%20Signals%20Source%20Identification%20System%20By%20Time-Frequency%20Analysis%20Technique.pdf http://eprints.utem.edu.my/id/eprint/24695/2/Voltage%20Variation%20Signals%20Source%20Identification%20System%20By%20Time-Frequency%20Analysis%20Technique.pdf
_version_	1747834090601578496

Voltage Variation Signals Source Identification System By Time-Frequency Analysis Technique

Similar Items