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Conference Papers Year : 2017

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.
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Dates and versions

hal-01578478 , version 1 (12-03-2020)

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Amel Adouni, Dhia Chariag, Claude Delpha, Demba Diallo, Lassaad Sbita. Statistical Analysis of Current-based Features for Dip Voltage Fault Detection and Isolation. 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Oct 2017, Beijing, China. ⟨10.1109/iecon.2017.8216748⟩. ⟨hal-01578478⟩
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