Intelligent fault detection and classification for a power transmission line using power system stabilizer signals

The analysis of how power system stabilizer (PSS) able to stabilize the power system efficiently during the transmission line is an important area of research in power operation and planning. One of the essential works of power system security is to operate and handle information on fault detection...

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Main Author:	Mat @ Mohamed, Usamah
Format:	Thesis
Language:	English
Published:	2009
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/12058/5/UsamahMatMohamedMFKE2009.pdf
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id	my-utm-ep.12058
record_format	uketd_dc
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mat @ Mohamed, Usamah Intelligent fault detection and classification for a power transmission line using power system stabilizer signals
description	The analysis of how power system stabilizer (PSS) able to stabilize the power system efficiently during the transmission line is an important area of research in power operation and planning. One of the essential works of power system security is to operate and handle information on fault detection effectively. In the proposed thesis, the oscillation for tow machine in “one phase fault”, “Fault with and without PSS”, “Fault with and without SVC”, are recorded at various fault locations. Multi Resolution Analysis (MRA) Wave Transform is used for fault detection. The MRA analyses the signal, where the statistical features for different locations and condition of the fault are extracted efficiently. The features are fed to Probabilistic Neural Network (PNN) to act as a fault classifier. The features are set as input vectors and the locations are set as the target. Graphic User Interface is used to monitor the whole system. When the fault is classified using PNN, its location can be used to generate control signals for PSS, which will be used to improve the stability in the power system. Therefore, this work shows the new techniques in detecting, classifying, and locating faults in a transmission line based on PSS signals as compared to traditional methods.
format	Thesis
qualification_level	Master's degree
author	Mat @ Mohamed, Usamah
author_facet	Mat @ Mohamed, Usamah
author_sort	Mat @ Mohamed, Usamah
title	Intelligent fault detection and classification for a power transmission line using power system stabilizer signals
title_short	Intelligent fault detection and classification for a power transmission line using power system stabilizer signals
title_full	Intelligent fault detection and classification for a power transmission line using power system stabilizer signals
title_fullStr	Intelligent fault detection and classification for a power transmission line using power system stabilizer signals
title_full_unstemmed	Intelligent fault detection and classification for a power transmission line using power system stabilizer signals
title_sort	intelligent fault detection and classification for a power transmission line using power system stabilizer signals
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2009
url	http://eprints.utm.my/id/eprint/12058/5/UsamahMatMohamedMFKE2009.pdf
_version_	1747814890772365312
spelling	my-utm-ep.120582017-09-17T06:59:34Z Intelligent fault detection and classification for a power transmission line using power system stabilizer signals 2009-11 Mat @ Mohamed, Usamah TK Electrical engineering. Electronics Nuclear engineering The analysis of how power system stabilizer (PSS) able to stabilize the power system efficiently during the transmission line is an important area of research in power operation and planning. One of the essential works of power system security is to operate and handle information on fault detection effectively. In the proposed thesis, the oscillation for tow machine in “one phase fault”, “Fault with and without PSS”, “Fault with and without SVC”, are recorded at various fault locations. Multi Resolution Analysis (MRA) Wave Transform is used for fault detection. The MRA analyses the signal, where the statistical features for different locations and condition of the fault are extracted efficiently. The features are fed to Probabilistic Neural Network (PNN) to act as a fault classifier. The features are set as input vectors and the locations are set as the target. Graphic User Interface is used to monitor the whole system. When the fault is classified using PNN, its location can be used to generate control signals for PSS, which will be used to improve the stability in the power system. Therefore, this work shows the new techniques in detecting, classifying, and locating faults in a transmission line based on PSS signals as compared to traditional methods. 2009-11 Thesis http://eprints.utm.my/id/eprint/12058/ http://eprints.utm.my/id/eprint/12058/5/UsamahMatMohamedMFKE2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering 1. I Kamwa, R Grondin, and G Trudel, “IEEE PSS2B Versus PSS4B: The Limit of Performance of Modern Power System Stabilizers”, IEEE Transaction on Power System, vol.20, no.2, May, 2005. 2. K Y Lee, H S Ko, H C Kim, J H Lee, and Y M Park, “A free Model Based Intelligent Controller design and Its Application to Power System Stabilizer” IEEE, pp1985-1989, 2000. 3. M A Abido, and Y L Abdel-Magid, “Power system stability Enhancement Via Coordinated Design of a PSS and SVC-Based Controller”, IEEE, Electrical Engineering, pp850-853, 2003. 4. M sanay, and H Zadeh, “Transmission Line Fault Detection & Phase Selection using ANN”, IPST Conference on Power System Transient, 2003. 5. M F Othman, M Mahfouf, and D S Linkens, “Transmission Line Fault Detection, classification and location using an Intelligent Power system stabilibizer”, IEEE Electric Utility, aptilligent , Ho Kong, on MR , Apti, 2004. 6. P Kundur, J Paserba, V Ajjarapu, G Andersson, A Bose, C Canizares, N Hatziagrgyriou, D Hill, A Stankovic, C Taylor, T V custem, and V Vittal, “Definition and Classification of Power System Stability”, IEEE Transaction on Power System, vol.19, No.2, May, 2004. 7. H A FALIFLA, “On Line Fault Detection for Transmission Line Using Power System stabilizer Signal”, 2007. 8. NWOHU Mark Mdubuka, “Voltage Stability Improvement using Var Compensator in Power Systems”, Leonardo Journal Sciences,Issue 14, January-June 2009. 9. V. Herrero, J. Cera, R. Gadea, M. Martines, A. Sebastia, “Impelementation of 1-D Daubechies Wavelet Transform on FPGA”, Universidad Politecnica Valencia, 2009 10. F Lee, “Conceptual Wavelets in Digital Signal Processing”, American Library Association, 2009. 11. Ajith Abraham,”Artificial Neural Network”, Oklaoma State University, 2009. 12. D Haward, B Mark, and H Martin,”Neural Network Tollbox 6 User Guide”, MathWorks, 2009. 13. R Grondin, I Kamwa, L Soulieres, J Potvin, and R Champagne, "An approach to PSS design for transient stability improvement through supplementary damping of the common low frequency," IEEE Transactions on Power Systems, 8(3), August 1993, pp. 954-963 14. M F Qureshi, J Kgabel, C S Khande, and I C Bhrti, “application of Hybrid system control for real-time Power System Stabilization”, ELSEVER Fuzzy sets and System, 2007 15. M zarringhalami, and M A Golkar, “Analysis of Power System Linearized Model with STATCOM Based Damping stabilizer”, K.N.oosi University Electrical Engineering, 6-9 April, 2008. 16. J Chen, J V Milanovic, and F M Hughes, “Comparison of the effectiveness of PSS and SVC in Damping Power System Oscillation” Department. of Electrical Engineering & Electronics pp183. 17. O P Malik, “ Adaptive and artificial Intelligence Based PSS” , IEEE University of Calgary, pp 1792-1797, 2003 18. A M Hemeida, G El-Saady, “Damping Power Systems Oscillations Using FACTS Combinations”, IEEE pp 732-735, 2004. 19. Z Jiang,“Design of a nonlinear power system stabilizer using synergetic control theory”, ELSEVIER, Electric Power Systems, November 2008 20. D Jovcic, G N Pillai "Analytical Modelling of TCSC Dynamics" IEEE® Transactions on Power Delivery, vol 20, Issue 2, pp. 1097-1104, April 2005. 21. IEEE recommended practice for excitation system models for power system stability studies: IEEE St. 421.5-2002(Section 9).

Intelligent fault detection and classification for a power transmission line using power system stabilizer signals

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