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Learning Geo-Temporal Non-Stationary Failure and Recovery of Power Distribution
[article]
2013
arXiv
pre-print
Smart energy grid is an emerging area for new applications of machine learning in a non-stationary environment. Such a non-stationary environment emerges when large-scale failures occur at power distribution networks due to external disturbances such as hurricanes and severe storms. Power distribution networks lie at the edge of the grid, and are especially vulnerable to external disruptions. Quantifiable approaches are lacking and needed to learn non-stationary behaviors of large-scale failure
arXiv:1304.7710v1
fatcat:btypkiwi2ndw3hn233ucb65osa