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Conference Papers Year : 2016

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

Amel Adouni
  • Function : Author
Claude Delpha
Lassad Sbita
  • Function : Author

Abstract

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.
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Dates and versions

hal-01390878 , version 1 (12-03-2020)

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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. 42nd Annual Conference of the IEEE Industrial Electronics Society (IECON 2016), IEEE, Oct 2016, Florence, Italy. pp.6971 - 6976, ⟨10.1109/IECON.2016.7793167⟩. ⟨hal-01390878⟩
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