A Novel Passive Islanding Detection Method Using DFIG Stator Flux

Unintentional islanding is a significant challenge in modern electrical power systems, jeopardising stability and safety. This article proposes an original passive approach to detecting islanding for a doubly fed induction generator-based wind generation system using four statistical features of the stator flux. The four statistical features are the kurtosis and skewness of the stator flux magnitude and the root mean square and mean of the absolute rate of change of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> axis component of the stator flux. Drawing from the simulation studies, specific thresholds of the statistical features can be established to effectively discriminate between islanding and non-islanding events. The proposed method also classifies islanding under a perfectly power-matched condition. Simulation studies in MATLAB R2023a/SIMULINK validate the proposed approach.