Voltage dip fault detection and identification based on Principal Component Analysis: Application to Wind Energy Conversion System

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.