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A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges

Year 2018, Volume: 18 Issue: 1, 72 - 77, 23.02.2018

Abstract

Signal quality is the key
issue for maintaining effective power transmission in electrical networks. In
most cases, a high voltage (HV) is transmitted in power systems to decrease
power loss. Power quality disturbances are monitored by observing the noise
degradation of HV signals. Increased oscillations and high-frequency components
of power signals exhibit nonstationary signal characteristics. In this study, a
comparative analysis of empirical mode decomposition (EMD) and variational mode
decomposition (VMD) was conducted on noisy discharge signals. These techniques
were used for adaptive signal decomposition in the time domain, facilitating
the evaluation of deeper characteristics of the investigated signal. The HV
discharges were obtained using 0.4/40 kV and 8 kVA transformers in a
laboratory, and all the current and voltage signal waveforms were recorded
using high-frequency current and high-voltage probes. The results demonstrate
distinct calculations of EMD and VMD techniques in terms of signal
decomposition and extracting intrinsic mode functions (IMFs), which define low-
and high-frequency components.

References

  • 1. J. Thorp, “Disturbance Analysis for Power Systems [Book Reviews]”, in IEEE Power and Energy Magazine, vol. 10, no. 3, pp. 89-90, May-June 2012. 2. M. Biswal, Y. Hao, P. Chen, S. Brahma, H. Cao, P. De Leon, “Signal features for classification of power system disturbances using PMU data”, In Power Systems Computation Conference (PSCC), pp. 1-7, 2016. 3. M. Sabarimalai Manikandan, S. R. Samantaray and I. Kamwa, “Simultaneous denoising and compression of power system disturbances using sparse representation on overcomplete hybrid dictionaries”, in IET Generation, Transmission & Distribution, vol. 9, no. 11, pp. 1077-1088, 2015. 4. L. Cai, N.F. Thornhill, B.C. Pal, “Multivariate Detection of Power System Disturbances Based on Fourth Order Moment and Singular Value Decomposition”, in IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4289-4297, Nov. 2017. 5. G. Rata, M. Rata, C. Filote, “Theoretical and experimental aspects concerning Fourier and wavelet analysis for deforming consumers in power network”, Elektronika Elektrotechnika (Electronics and Electrical Engineering), no. 1. pp. 62-66, 2010. 6. Z. Guo, L. Xie, A. Horch, Y. Wang, H. Su, X. Wang, “Automatic Detection of Nonstationary Multiple Oscillations by an Improved Wavelet Packet Transform”, Industrial and Engineering Chemical Research, vol. 53, no. 40, pp. 15686-15697, 2014. 7. A. N. I. Wardana, “A comparative study of EMD, EWT and VMD for detecting the oscillation in control loop”, International Seminar on Application for Technology of Information and Communication (ISemantic), Semarang, pp. 58-63, 2016. 8. N. E. Huang, Z. Shen, S.R. Long, M. C. Wu, H. H. Shih, Q. Zheng, H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”, In Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences, vol. 454, no. 1971, pp. 903-995, 1998. 9. Y. Lei, J. Lin, Z. He, M.J. Zuo, “A review on empirical mode decomposition in fault diagnosis of rotating machinery”, Mechanical Systems and Signal Processing, vol.35, no.1, pp.108-126, 2013. 10. D. P. Mandic, N. Rehman, Z. Wu, N. E. Huang, “Empirical mode decomposition-based time-frequency analysis of multivariate signals: The power of adaptive data analysis”, IEEE signal processing magazine, vol. 30, no. 6, pp. 74-86, 2013. 11. J. Dybala, R. Zimroz, “Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal”, Applied Acoustics, vol. 77, pp.195-203, 2014. 12. S. Lahmiri, “Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains”, in Healthcare Technology Letters, vol. 1, no. 3, pp. 104-109, 2014. 13. G. Georgoulas, I.P. Tsoumas, J.A. Antonino-Daviu, V. Climente-Alarcon, C.D. Stylios, E.D. Mitronikas, A.N. Safacas, “Automatic pattern identification based on the complex empirical mode decomposition of the startup current for the diagnosis of rotor asymmetries in asynchronous machines”, IEEE Transactions on Industrial Electronics, vol. 61, no. 9, pp. 4937-4946, 2014. 14. K. Dragomiretskiy, D. Zosso, “Variational Mode Decomposition”, in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb. 1, 2014. 15. L. Zhao, C. Li, Y. Zhu, X. Chen, X. Guo, Y. Gao, “Power Transformer Partial Discharge Signal De-noising Based on Variational Mode Decomposition”, International Conference on Intelligent Systems Research and Mechatronics Engineering, pp. 819-822, 2015. 16. Y. Wang, R. Markert, J. Xiang, W. Zheng, “Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system”, Mechanical Systems and Signal Processing, vol. 60, pp. 243-251, 2015. 17. A. Upadhyay, R.B. Pachori, “Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition”, In Journal of the Franklin Institute, vol. 352, no. 7, pp. 2679-2707, 2015. 18. U. Maji, S. Pal, “Empirical mode decomposition vs. variational mode decomposition on ECG signal processing: A comparative study”, International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, pp. 1129-1134, 2016. 19. K. P. Soman, P. Poornachandran, S. Athira, K. Harikumar, “Recursive Variational Mode Decomposition Algorithm for Real Time Power Signal Decomposition”, In Procedia Technology, vol. 21, pp. 540-546, 2015. 20. G. Rilling, P. Flandrin, P. Goncalv`es, “On empirical mode decomposition and its algorithms”, in IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03, 2003. 21. P. Flandrin, G. Rilling, P. Gonc¸alv`es, “Empirical mode decomposition as a filter bank”, IEEE Sig. Proc. Lett., vol. 11, no. 2, pp. 112-114, 2004. 22. N. Pan, V. Mang, M. P. Un, P. S. Hang, “Accurate Removal of Baseline Wander in ECG Using Empirical Mode Decomposition,” 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, Hangzhou, 2007, pp. 177-180.
Year 2018, Volume: 18 Issue: 1, 72 - 77, 23.02.2018

Abstract

References

  • 1. J. Thorp, “Disturbance Analysis for Power Systems [Book Reviews]”, in IEEE Power and Energy Magazine, vol. 10, no. 3, pp. 89-90, May-June 2012. 2. M. Biswal, Y. Hao, P. Chen, S. Brahma, H. Cao, P. De Leon, “Signal features for classification of power system disturbances using PMU data”, In Power Systems Computation Conference (PSCC), pp. 1-7, 2016. 3. M. Sabarimalai Manikandan, S. R. Samantaray and I. Kamwa, “Simultaneous denoising and compression of power system disturbances using sparse representation on overcomplete hybrid dictionaries”, in IET Generation, Transmission & Distribution, vol. 9, no. 11, pp. 1077-1088, 2015. 4. L. Cai, N.F. Thornhill, B.C. Pal, “Multivariate Detection of Power System Disturbances Based on Fourth Order Moment and Singular Value Decomposition”, in IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4289-4297, Nov. 2017. 5. G. Rata, M. Rata, C. Filote, “Theoretical and experimental aspects concerning Fourier and wavelet analysis for deforming consumers in power network”, Elektronika Elektrotechnika (Electronics and Electrical Engineering), no. 1. pp. 62-66, 2010. 6. Z. Guo, L. Xie, A. Horch, Y. Wang, H. Su, X. Wang, “Automatic Detection of Nonstationary Multiple Oscillations by an Improved Wavelet Packet Transform”, Industrial and Engineering Chemical Research, vol. 53, no. 40, pp. 15686-15697, 2014. 7. A. N. I. Wardana, “A comparative study of EMD, EWT and VMD for detecting the oscillation in control loop”, International Seminar on Application for Technology of Information and Communication (ISemantic), Semarang, pp. 58-63, 2016. 8. N. E. Huang, Z. Shen, S.R. Long, M. C. Wu, H. H. Shih, Q. Zheng, H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”, In Proceedings of the Royal Society of London A: mathematical, physical and engineering sciences, vol. 454, no. 1971, pp. 903-995, 1998. 9. Y. Lei, J. Lin, Z. He, M.J. Zuo, “A review on empirical mode decomposition in fault diagnosis of rotating machinery”, Mechanical Systems and Signal Processing, vol.35, no.1, pp.108-126, 2013. 10. D. P. Mandic, N. Rehman, Z. Wu, N. E. Huang, “Empirical mode decomposition-based time-frequency analysis of multivariate signals: The power of adaptive data analysis”, IEEE signal processing magazine, vol. 30, no. 6, pp. 74-86, 2013. 11. J. Dybala, R. Zimroz, “Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal”, Applied Acoustics, vol. 77, pp.195-203, 2014. 12. S. Lahmiri, “Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains”, in Healthcare Technology Letters, vol. 1, no. 3, pp. 104-109, 2014. 13. G. Georgoulas, I.P. Tsoumas, J.A. Antonino-Daviu, V. Climente-Alarcon, C.D. Stylios, E.D. Mitronikas, A.N. Safacas, “Automatic pattern identification based on the complex empirical mode decomposition of the startup current for the diagnosis of rotor asymmetries in asynchronous machines”, IEEE Transactions on Industrial Electronics, vol. 61, no. 9, pp. 4937-4946, 2014. 14. K. Dragomiretskiy, D. Zosso, “Variational Mode Decomposition”, in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb. 1, 2014. 15. L. Zhao, C. Li, Y. Zhu, X. Chen, X. Guo, Y. Gao, “Power Transformer Partial Discharge Signal De-noising Based on Variational Mode Decomposition”, International Conference on Intelligent Systems Research and Mechatronics Engineering, pp. 819-822, 2015. 16. Y. Wang, R. Markert, J. Xiang, W. Zheng, “Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system”, Mechanical Systems and Signal Processing, vol. 60, pp. 243-251, 2015. 17. A. Upadhyay, R.B. Pachori, “Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition”, In Journal of the Franklin Institute, vol. 352, no. 7, pp. 2679-2707, 2015. 18. U. Maji, S. Pal, “Empirical mode decomposition vs. variational mode decomposition on ECG signal processing: A comparative study”, International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, pp. 1129-1134, 2016. 19. K. P. Soman, P. Poornachandran, S. Athira, K. Harikumar, “Recursive Variational Mode Decomposition Algorithm for Real Time Power Signal Decomposition”, In Procedia Technology, vol. 21, pp. 540-546, 2015. 20. G. Rilling, P. Flandrin, P. Goncalv`es, “On empirical mode decomposition and its algorithms”, in IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03, 2003. 21. P. Flandrin, G. Rilling, P. Gonc¸alv`es, “Empirical mode decomposition as a filter bank”, IEEE Sig. Proc. Lett., vol. 11, no. 2, pp. 112-114, 2004. 22. N. Pan, V. Mang, M. P. Un, P. S. Hang, “Accurate Removal of Baseline Wander in ECG Using Empirical Mode Decomposition,” 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, Hangzhou, 2007, pp. 177-180.
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Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Cengiz Polat Uzunoğlu

Publication Date February 23, 2018
Published in Issue Year 2018 Volume: 18 Issue: 1

Cite

APA Uzunoğlu, C. P. (2018). A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges. Electrica, 18(1), 72-77.
AMA Uzunoğlu CP. A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges. Electrica. February 2018;18(1):72-77.
Chicago Uzunoğlu, Cengiz Polat. “A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges”. Electrica 18, no. 1 (February 2018): 72-77.
EndNote Uzunoğlu CP (February 1, 2018) A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges. Electrica 18 1 72–77.
IEEE C. P. Uzunoğlu, “A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges”, Electrica, vol. 18, no. 1, pp. 72–77, 2018.
ISNAD Uzunoğlu, Cengiz Polat. “A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges”. Electrica 18/1 (February 2018), 72-77.
JAMA Uzunoğlu CP. A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges. Electrica. 2018;18:72–77.
MLA Uzunoğlu, Cengiz Polat. “A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges”. Electrica, vol. 18, no. 1, 2018, pp. 72-77.
Vancouver Uzunoğlu CP. A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges. Electrica. 2018;18(1):72-7.