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Energies 2016, 9(3), 211; doi:10.3390/en9030211

Indices to Study the Electrical Power Signals in Active and Passive Distribution Lines: A Combined Analysis with Empirical Mode Decomposition

1
Department of Electrical and Information Engineering, Politecnico di Bari, via E. Orabona 4, Bari I-70125, Italy
2
Department of Physics and Earth Sciences and Consorzio Nazionale Interuniversitario per le Scienze Fisiche Unit of Ferrara, University of Ferrara, via Saragat 1, Ferrara I-44122, Italy
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Rodolfo Araneo
Received: 13 January 2016 / Revised: 24 February 2016 / Accepted: 4 March 2016 / Published: 17 March 2016
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Abstract

The broad diffusion of renewable energy-based technologies has introduced several open issues in the design and operation of smart grids (SGs) when distributed generators (DGs) inject a large amount of power into the grid. In this paper, a theoretical investigation on active and reactive power data is performed for one active line characterized by several photovoltaic (PV) plants with a great amount of injectable power and two passive lines, one of them having a small peak power PV plant and the other one having no PV power. The frequencies calculated via the empirical mode decomposition (EMD) method based on the Hilbert-Huang transform (HHT) are compared to the ones obtained via the fast Fourier transform (FFT) and the wavelet transform (WT), showing a wider spectrum of significant modes mainly due to the non-periodical behavior of the power signals. The results obtained according to the HHT-EMD analysis are corroborated by the calculation of three new indices that are computed starting from the electrical signal itself and not from the Hilbert spectrum. These indices give the quantitative deviation from the periodicity and the coherence degree of the power signals, which typically deviate from the stationary regime and have a nonlinear behavior in terms of amplitude and phase. This information allows to extract intrinsic features of power lines belonging to SGs and this is useful for their optimal operation and planning. View Full-Text
Keywords: coherence degree; periodicity degree; Wavelet Transform; Empirical Mode Decomposition; Hilbert-Huang Transform; Smart Grids coherence degree; periodicity degree; Wavelet Transform; Empirical Mode Decomposition; Hilbert-Huang Transform; Smart Grids
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. (CC BY 4.0).

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Vergura, S.; Zivieri, R.; Carpentieri, M. Indices to Study the Electrical Power Signals in Active and Passive Distribution Lines: A Combined Analysis with Empirical Mode Decomposition. Energies 2016, 9, 211.

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