Modeling of Electric Disturbance Signals Using Damped Sinusoids via Atomic Decompositions and Its Applications
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Research Article Modeling of Electric Disturbance Signals Using Damped Sinusoids via Atomic Decompositions and Its Applications Lisandro Lovisolo,1 Michel P. Tcheou,2, 3 Eduardo A. B. da Silva,2 Marco A. M. Rodrigues,3 and Paulo S. R. Diniz2 1 Departamento
de Eletrˆonica e Telecomunicac¸o˜es (DETEL), Faculdade de Engenharia (FEN), Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro 20550-900, RJ, Brazil 2 Laboratory of Signal Processing, PEE/COPPE and DEL/Poli, Federal University of Rio de Janeiro, CP 68504, Rio de Janeiro 21941-972, RJ, Brazil 3 Electric Power Research Center (CEPEL), CP 68007, Rio de Janeiro 21941-590, RJ, Brazil Received 10 August 2006; Accepted 17 December 2006 Recommended by Alexander Mamishev The number of waveforms monitored in power systems is increasing rapidly. This creates a demand for computational tools that aid in the analysis of the phenomena and also that allow efficient transmission and storage of the information acquired. In this context, signal processing techniques play a fundamental role. This work is a tutorial reviewing the principles and applications of atomic signal modeling of electric disturbance signals. The disturbance signal is modeled using a linear combination of damped sinusoidal components which are closely related to the phenomena typically observed in power systems. The signal model obtained is then employed for disturbance signal denoising, filtering of “DC components,” and compression. Copyright © 2007 Lisandro Lovisolo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Electric disturbance signals are acquired by digitizing the voltage and/or current waveforms with digital fault recorders (DFRs) at several points of the power system network. Figure 1 illustrates a typical DFR data, composed by the voltage and current waveforms of a three-phase system and the correspondent neutrals in a transmission line. In Figure 1, we can observe the three main parts of interest for fault analysis. The prefault shows the system behavior prior to the fault occurrence and the postfault shows the system state after fault recovering. Along with fault signals, power quality events are also acquired in order to monitor transient behavior and evaluate the impacts of power consumer apparatuses on the power quality. The analysis of disturbance signals allows the identification of patterns and characteristics of faults and also to assess power quality [1–6]. The number of points monitored in power systems is increasing rapidly because: (a) the power system operation bounds get more critical as demand increases; (b) at large interconnected systems, it is necessary to establish precisely the causes of the disturbance as well as the responsibilities for the
resulting effects. Storage and transmission of disturbance signals may generate an information overload, even though the cost of storag
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