Mario Ortiz, Sergio Valero, Antonio Gabaldón, and Carlos Álvarez
[1] S. Santoso, E.J. Powers, and W.M. Grady, Power quality disturbance data compression using wavelet transform methods, IEEE Transactions on Power Delivery, 12(3), 1997, 1250–1255. [2] M. Ortiz, S. Valero, and A. Gabaldón, Transient power and quality events analysed using Hilbert transforms, Journal of Energy and Power Engineering, 6(2), 2012, 230–239. [3] D.R. Ostojic and G.T. Heydt, Transient stability assessment by pattern recognition in the frequency domain, IEEE Transactions on Power Systems, 6(1), 1991, 231–237. [4] G.T. Heydt, P.S. Field, C.C. Liu, D. Pierce, et al., Applications of the windowed FFT to electric power quality assessment, IEEE Transactions on Power Delivery, 14(4), 1999, 1411–1416. [5] F.H. Magnano and A. Abur, Fault location using wavelets, IEEE Transactions on Power Delivery, 13(4), 1998, 1475–1478. [6] S. Santoso, E.J. Powers, W.M. Grady, and P. Hofmann, Power quality assessment via wavelet transform analysis, IEEE Transactions on Power Delivery, 11(2), 1996, 924–930. [7] G.T. Heydt and A.W. Galli, Transient power quality problems analyzed using wavelets, IEEE Transactions on Power Delivery, 12(2), 1997, 908–915. [8] P. Pillay and A. Bhattacharjee, Application of wavelets to model short-term power system disturbances, IEEE Transactions on Power Systems, 4(11), 1996, 2031–2040. [9] I.W.C. Lee and P.K. Dash, S-transform-based intelligent system for classification of power quality disturbance signals, IEEE Transactions on Industrial Electronics, 50(4), 2003, 800–805. [10] A.M. Gargoom, N. Ertugrul, and W.L. Soong, Investigation of effective automatic recognition systems of power-quality events, IEEE Transactions on Power Delivery, 22(4), 2007, 2319–2326. [11] N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Procedures of the Royal Society of London, 454(1971), 1998, 903–995. [12] D.S. Laila, A.R. Messina, and B.C. Pal, A refined Hilbert–Huang transform with applications to interarea oscillation monitoring, IEEE Transactions on Power Systems, 24(2), 2009, 610–620. [13] T.J. Browne, V. Vittal, G.T. Heydt, and A.R. Messina, A comparative assessment of two techniques for modal identification from power system measurements, IEEE Transactions on Power Systems, 23(3), 2008, 1408–1415. [14] N. Senroy, S. Suryanarayanan, and P.F. Ribeiro, An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality, IEEE Transactions on Power Systems, 22(4), 2007, 1843–1850. [15] M.H.J. Bollen, E. Styvaktakis, and I.Y. Gu, Categorization and analysis of power system transients, IEEE Transactions on Power Delivery, 20(3), 2005, 2298–2306. [16] S.L. Hahn, Hilbert transforms in signal processing (Norwood, Maryland: Artech House, 1996). [17] S. Kizhner, T.P. Flatley, N.E. Huang, K. Blank, and K.E. Conwell, On the Hilbert–Huang transform data processing system development, IEEE Aerospace Conference Proceedings, 3, 2004, 1961–1975. [18] N.E. Huang and S.S.P. Shen, Hilbert–Huang transform and its applications (World Scientific Publishing Company, 2005). [19] G. Rilling, P. Flandrin, and P. Gonçalvès, On empirical mode decomposition and its algorithms, Proc. IEEE-EURASIP Workshop on Nonlinear Signal Image Process, Grado, Italy, 2003. [20] R. Deering and J.F. Kaiser, The use of a masking signal to improve empirical mode decomposition, Proc. IEEE Int. Conf. Acoustic, Speech, and Signal Processing, 4, 2005, 485–488. [21] http://perso.ens-lyon.fr/patrick.flandrin/emd.html, (2007) release. [22] T. Kohonen, Self-organisation and associative memory, 3rd ed. (Springer-Verlag, 1989). [23] S.V. Verdu, M.O. Garcia, C. Senabre, A.G. Marin, and F.J.G. Franco, Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps, IEEE Transactions on Power Systems, 21(4), 2006, 1672–1682. [24] IEEE Recommended Practice for Monitoring Electric Power Quality, IEEE Std., 1159-2009.
Important Links:
Go Back
