Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
Paper recorded by Signals 4 on 2026-09-09 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10
Category: cs.LG · 机器学习 · first seen 2026-09-10
Abstract
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
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Cite this page: Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems: the #87 most recent of 215 cs.LG papers we have recorded (as of 2026-09-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/deep-learning-based-detection-of-electrical-faults-and-power-quality-disturbance.html
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