Voltage Dip fault Detection and Identification based on Principal Component Analysis : application to Wind Energy Conversion System

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

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

This paper proposes a method for voltage dip fault voltage detection and diagnosis in a grid connected Wind Turbine Generator. The method is data-driven. From the measurements of the currents flowing into the grid, three features related to the trajectory of the current vector in the Concordia stationary reference frame are extracted. The evaluation of the features for fault diagnosis is done through Principal Component Analysis. In the subspaces spanned by the principal components (2D or 3D) the faults are detected and isolated. Simulation results prove the efficiency of the method.

Mots clés: Principal component analysis; Voltage fluctuations; Fault diagnosis; Wind turbines; Feature extraction; Fault detection; Trajectory; wind power plants

Domaines: Traitement du signal et de l'image [eess.SP]

Amel Adouni, Claude Delpha, Demba Diallo, Lassaad Sbita. Voltage Dip fault Detection and Identification based on Principal Component Analysis : application to Wind Energy Conversion System. 24th International Symposium on Industrial Electronics - ISIE 2015, Jun 2015, Rio de Janeiro, Brazil. ⟨10.1109/ISIE.2015.7281584⟩. ⟨hal-01169223⟩