Explores a new fault detection method in wind farm feeder lines, suggesting improved reliability.
Faults in wind farm multi-branch collector lines produce weak transient zero-mode current characteristics, rendering conventional amplitude- and phase-based selection methods ineffective. To address this, the paper proposes a faulty feeder selection method that integrates transient zero-mode differential current with the S-transform. A π-type zero-mode network is first established to reveal the composition and variation in transient zero-mode current under different operating conditions. Theoretical analysis demonstrates a distinguishable feature—the sending-end current of the faulted branch exhibits significantly larger amplitude and opposite polarity compared with non-faulted branches—while the end-side currents near wind turbines remain highly similar and thus hinder accurate selection. Motivated by this disparity, the adaptive time–frequency focusing property of the S-transform is exploited to extract high-frequency energy and differential signatures within the optimal band, from which a dual criterion consisting of the differential feature quantity Dm and the time–frequency energy Em is constructed. The method is validated on a 35 kV DFIG-based wind farm collector line model built in PSCAD/EMTDC and benchmarked against the wavelet transform and empirical mode decomposition under various operating conditions. The results confirm that the proposed method accurately distinguishes bus faults from branch faults, reliably identifies the faulty feeder, and remains unaffected by fault distance and operating conditions, demonstrating strong robustness and anti-interference capability.
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Duan et al. (2026) studied this question.
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