Statistical analysis of current-based features for dip voltage fault detection and isolation

Abstract

The high penetration of Wind Turbine (WT) in the grid is a promising solution to increase the electricity production with renewable energies. In this work, we propose a data-driven methodology for dip voltage fault detection and diagnosis. From experimental measurements the current vector trajectory deformation in the (αβ) reference frame is derived and a statistical-based analysis (first four statistical moments) of two relevant features are extracted (the ratio between the two axis and the rotation angle) is conducted. Thanks to this ratio, the method is robust to load variations. The fault isolation is done accurately with the analysis of the shift angle. The fault detection performances are evaluated with the ROC curves that reveal a probability of detection equal to 1 and a null probability of false alarm.

Dynamic grid fault analysis in wind power plant with DFIG by using supervisory control and data acquisition (SCADA) viewer

“…The Permanent Magnetic Doubly Fed Induction Generator (PMDFIG) was used to solve an issue of voltage fluctuation but still the poor communication issue was still there (the grid faults lead to the voltage sag in short duration at the feed point) [8]. The communication issue was little bit solved but the overloading issue for intermediate circuit located between machine side controller and line side controller isn't used [9].…”