Data-driven approach for dip voltage fault detection and identification based on grid current vector trajectory analysis

Amel Adouni , Claude Delpha (1) , Demba Diallo (2) , Lassad Sbita

1 L2S - Laboratoire des signaux et systèmes
2 GeePs - Laboratoire Génie électrique et électronique de Paris

This paper proposes a data driven approach for dip voltage fault detection and identification using the grid current vector trajectory in the stationary reference frame. Three features are extracted for the different operating conditions to build the database and analysed using Linear Discriminant Analysis to identify the fault type and subtype. In the subspaces spanned by the factorial components the four faults and eight out of nine faults subtype are successfully identified and isolated with an error rate less than 5%. Simulation results prove the efficiency of the proposed algorithm.

Keywords: Dip Voltage; Grid current; fault detection; fault identification; Linear Discriminant Analysis

Domains: Signal and Image processing

Amel Adouni, Claude Delpha, Demba Diallo, Lassad Sbita. Data-driven approach for dip voltage fault detection and identification based on grid current vector trajectory analysis. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, IEEE, Oct 2016, Florence, Italy. pp.6971 - 6976, ⟨10.1109/IECON.2016.7793167⟩. ⟨hal-01390878⟩