Dergiler / Electrica / 2018 / Cilt: 18 - Sayı: 1

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

Sayfa
72–77
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Signal quality is the key issue for maintaining effective power transmission in electrical networks.
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Temel Bulgular & Veri:In most cases, a high voltage (HV) is transmitted in power systems to decrease power loss.
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Sonuç & Katkı: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.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

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.

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Bu Makaleye Atıf Yap

UZUNOĞLU, C. P. (2018). A Comparative Study of Empirical and Variational Mode Decomposition on High Voltage Discharges. Electrica, Cilt: 18 - Sayı: 1, 72–77. https://ajindex.com/articles/153536

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Kaynakça (2)

  1. J. Thorp, “Disturbance Analysis for Power Systems [Book Reviews]”, in IEEE Power and Energy Magazine, vol. 10, no. 3, pp. 89-90, MayJune 2012. [CrossRef] 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. [CrossRef] 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. [CrossRef] 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. [CrossRef] 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.
  2. 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. [CrossRef] 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. [CrossRef] 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. [CrossRef] 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. [CrossRef] 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. [CrossRef] J. Dybala, R. Zimroz, “Rolling bearing diagnosing method based on empirical