Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
More Electric Aircraft require fast and reliable monitoring of high-frequency electrical networks, yet most power quality disturbance and fault diagnosis methods are developed for conventional 50 or 60 Hz grids. This work presents a hardware-aware deep learning framework for multiclass detection of electrical faults and power quality disturbances in a 400 Hz aerospace power system. A high-fidelity simulation model inspired by the Boeing 787 electrical architecture generates voltage and current waveforms for 21 normal, disturbance, switching, open-circuit, and short-circuit conditions. Two datasets, each containing 73,500 samples, are formed from one-dimensional time-series signals and sho...
| Authors | Ian C. Guzmán, Radu Babiceanu, Berker Peköz |
|---|---|
| Primary category | eess.SP |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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