Smart Grid Voltage Sag Detection using Instantaneous Features Extraction

Yassine Amirat (1) , Mohamed Benbouzid (1) , Wang Tianzhen (2) , Sylvie Turri (1)

1 LBMS - Laboratoire brestois de mécanique et des systèmes
2 Shanghai Maritime University

Smart grids have initiated a radical reappraisal of distribution networks function where the integration of renewable energy sources, load demand control, and effective use of the network are indexed as the most important keys for smart grid expansion and deployment regardless each country policies. One of the most efficient ways of effective use of these grids would be to continuously monitor their conditions. This allows for early detection of power quality degeneration facilitating therefore a proactive response, prevent a fault ride-through the renewable power sources, minimizing downtime, and maximizing productivity. In this smart grid context, this paper proposes the evaluation and comparison of advanced signal processing tools, namely the Hilbert transform and the ensemble empirical mode decomposition method for the detection of voltage sags as they are the most commonly encountered power quality disturbances.

Mots clés: Smart grid; voltage sag detection; power quality (PQ); Hilbert Transform (HT); ensemble empirical mode decomposition (EEMD)

Domaines: Energie électrique

Yassine Amirat, Mohamed Benbouzid, Wang Tianzhen, Sylvie Turri. Smart Grid Voltage Sag Detection using Instantaneous Features Extraction. IEEE IECON 2013, Nov 2013, Vienne, Austria. pp.7394-7399. ⟨hal-00926235⟩