EN
Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform
Abstract
This paper presents a new method based emprical mode decomposition and hilbert transform for detection of power quality disturbances. Hilbert Huang Transform HHT, which was suggested by Huang and improved by Flandrin and his group, is a new signal processing method. The analysis of nonlinear and non stationary signals can use HHT. In this paper, the recorded disturbances signals are decomposed into Intrinsic Mode Functions using the Emprical Mode Decomposition. The frequency and amplitude of power disturbances are obtained of IMF components by using Hilbert Transform HT. The clear success of EMD in defining envelope variations of a sinusoidal waveform has been the main motivation for the adoption of EMD in analysis of power disturbance signals. Simulations are performed over waveforms including voltage harmonics, voltage sag and swell. The waveforms are selected as pure sinusoids. Simulation results show that the suggested methodology can effectively detect different power disturbances.
References
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- Gu Y. H., Bollen M. H. J., “Time-frequency and time-scale domain analysis of voltage disturbances”, IEEE Trans. Power Del., vol. 15, pp. 1279–1284, Oct. 2000.
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Details
Primary Language
Turkish
Subjects
-
Journal Section
-
Publication Date
June 1, 2016
Submission Date
July 1, 2016
Acceptance Date
-
Published in Issue
Year 2015 Volume: 2 Number: 1
APA
Önal, Y., Gerek, Ö. N., & Ece, D. G. (2016). Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 2(1), 27-34. https://izlik.org/JA37GH92LT
AMA
1.Önal Y, Gerek ÖN, Ece DG. Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi. 2016;2(1):27-34. https://izlik.org/JA37GH92LT
Chicago
Önal, Yasemin, Ömer Nezih Gerek, and Doğan Gökhan Ece. 2016. “Detection of Power Disturbances Using Emprical Mode Decomposition and Hilbert Transform”. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 2 (1): 27-34. https://izlik.org/JA37GH92LT.
EndNote
Önal Y, Gerek ÖN, Ece DG (June 1, 2016) Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 2 1 27–34.
IEEE
[1]Y. Önal, Ö. N. Gerek, and D. G. Ece, “Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform”, Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, vol. 2, no. 1, pp. 27–34, June 2016, [Online]. Available: https://izlik.org/JA37GH92LT
ISNAD
Önal, Yasemin - Gerek, Ömer Nezih - Ece, Doğan Gökhan. “Detection of Power Disturbances Using Emprical Mode Decomposition and Hilbert Transform”. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 2/1 (June 1, 2016): 27-34. https://izlik.org/JA37GH92LT.
JAMA
1.Önal Y, Gerek ÖN, Ece DG. Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi. 2016;2:27–34.
MLA
Önal, Yasemin, et al. “Detection of Power Disturbances Using Emprical Mode Decomposition and Hilbert Transform”. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, vol. 2, no. 1, June 2016, pp. 27-34, https://izlik.org/JA37GH92LT.
Vancouver
1.Yasemin Önal, Ömer Nezih Gerek, Doğan Gökhan Ece. Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi [Internet]. 2016 Jun. 1;2(1):27-34. Available from: https://izlik.org/JA37GH92LT