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Towards a Real-time Measurement Platform for Microgrids in Isolated Communities
Author(s) -
Geir Kulia,
Marta Molinas,
Lars Lundheim,
Bjørn B. Larsen
Publication year - 2016
Publication title -
procedia engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.32
H-Index - 74
ISSN - 1877-7058
DOI - 10.1016/j.proeng.2016.08.070
Subject(s) - microgrid , waveform , voltage , nonlinear system , grid , signal (programming language) , electronic engineering , hilbert–huang transform , amplitude , instantaneous phase , computer science , engineering , electrical engineering , physics , mathematics , geometry , filter (signal processing) , quantum mechanics , programming language
This paper describes a platform for obtaining and analyzing real-time measurements in Microgrids. A key building block in this platform is the Empirical Mode Decomposition (EMD) used to analyze the electrical voltage and current waveforms to identify the instantaneous frequency and amplitude of the monocomponents of the original signal. The method was used to analyse the frequency fluctuation and obtain information about the linearity of electrical current and voltage waveforms measured in the field. Comparison between grid-connected and stand-alone microgrid voltage and currents’ monocomponents were conducted. Fluctuations in the grid frequency occurred in both the grid-connected and stand-alone microgrid, but the degree of the observed fluctuations were different, revealing more apparent nonlinear distortions in the latter. The observed instantaneous frequency from the collected data indicates potential nonstationary electrical signals when compared to synthetic data containing periodic signals coming from nonlinear loads. This observation leads us to expect the next generation of real-time measuring devices for the micro power grids to be designed on the principle of instantaneous frequency detection. Further efforts will be directed to a more rigorous characterization of the nonstationary nature of the signals by analyzing more and longer set of data

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