FD-Mamba With Neural Observer and Frequency-Enhanced Update for Incipient Feeder Fault Detection

In distribution networks, incipient faults often manifest as faint and transient electrical disturbances before fully developing. Incipient fault detection is challenging due to the weak and non-stationary characteristics of fault signatures. Moreover, fault feeder identification is more difficult, as residuals across feeders tend to appear highly similar. To address these challenges, we present FD-Mamba, a Mamba-based neural state-space model that integrates control-theoretic principles with signal-processing techniques. Specifically, we propose a Kalman-inspired neural correction mechanism that performs residual-driven state updates with learnable gain factors. In addition, we introduce a frequency-momentum updating mechanism that stabilizes frequency tracking under non-stationary perturbations. Experimental results on two datasets show that FD-Mamba outperforms existing methods. It achieves a root mean square error of 0.427 and fault feeder detection accuracy of 98.1% on real-world field dataset.