Detection of Voltage Sag and Voltage Swell in Power Quality Using Wavelet Transforms
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Detection of Voltage Sag and Voltage Swell in Power Quality Using Wavelet Transforms

Authors: Nor Asrina Binti Ramlee

Abstract:

Voltage sag, voltage swell, high-frequency noise and voltage transients are kinds of disturbances in power quality. They are also known as power quality events. Equipment used in the industry nowadays has become more sensitive to these events with the increasing complexity of equipment. This leads to the importance of distributing clean power quality to the consumer. To provide better service, the best analysis on power quality is very vital. Thus, this paper presents the events detection focusing on voltage sag and swell. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. Four types of mother wavelet namely Haar, Dmey, Daubechies, and Symlet are used to detect the events. This project analyzed real interrupted signal obtained from 22 kV transmission line in Skudai, Johor Bahru, Malaysia. The signals will be decomposed through the wavelet mothers. The best mother is the one that is capable to detect the time location of the event accurately.

Keywords: Power quality, voltage sag, voltage swell, wavelet transform.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131637

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References:


[1] Pierre Kreidi, Course EE6723, Power Quality, University of New Brunswick.
[2] Gokhale, Khanduja, Daljeet (2010), Time Domain Signal Analysis Using Wavelet Packet Decomposition Approach, International Journal of Communications. Network and Systems Sciences (IJCNS).
[3] Alexander Apostolov, (2003), Detection and Recording of Power Quality Events in Distribution Systems, Fault and Disturbance Analysis Conference, Atlanta,Georgia.
[4] George, Georg, Perry, Analysis Using the Discrete Wavelet Transform, Computer Science Department, Princeton.
[5] Zwe-Lee Gaing (2003), Implementation of Power Disturbance Classifier Using Wavelet-Based Neural Networks, IEEE Bologna PowerTech Conference, Italy.
[6] Daljeet, Gokhale, Time Domain Signal Analysis Using Modified Haar and Modified Daubechies Wavelet Transform, Signal Processing-An International Journal (SPIJ), Volume (4): Issue (3) 161.
[7] T. Lachman, A.P. Menon, T.R. Mohamad (2010), Detection of Power Quality Disturbances Using Wavelet Transform Technique, International Journal for The Advancement of Science and Arts, Vol. 1, No 1.
[8] Gonzalez, Moreno, Two applications for Power Quality Analysis using the Matlab Wavelet Toolbox, Department of Electric Electronic and Technology Electronic, University of Cordoba.
[9] M. Sushama, Dr. G. Tulasi Ram Das (2008), Detection and Classification of Voltage Swells Using Adaptive Decomposition and Wavelet Transforms, Journal of Theoretical and Applied Information Technology (JATIT).
[10] Ying HK, Jin SY, Jing A (2007), Online Power Quality Disturbances Detection and Classification using One-Pass Wavelet Decomposition and Decision Tree, Machine Learning and Cybernetics International Conference, Vol.
[11] P. Chandrasekar, V. Kamaraj (2010), Detection and Classification of Power Quality Disturbance Waveform Using MRA Based Modifified Wavelet Transform and Neural Networks, Journal of Lectrical Engineering, Vol. 61, No. 4, pp 235-240.
[12] Sharmeela, Mohan, Uma, Baskaran (2006), A Novel Detection and Classification Algorithm for Power Quality Disturbances using Wavelets, American Journal of Applied Sciences 3 (10): pp 2049-2053, College of Engineering, Anna University, Chennai.