Abstract
Sheath current is one of the key indicators of underground power cable conditions. Considering the limitations of existing model-based methods for sheath current monitoring and difficulty in handling the increasing amount of unlabeled sheath current data accumulated by cable monitoring systems, we propose a data mining method based on unsupervised learning and spatiotemporal analysis of sheath currents for underground power cable monitoring. Tests based on real historical data demonstrate that the proposed method can effectively reveal unknown inherent patterns in unlabeled sheath current data.
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