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Securing Smart Grids with Machine Learning
Joule ( IF 39.8 ) Pub Date : 2020-03-18 , DOI: 10.1016/j.joule.2020.02.013
Brandon R. Sutherland

The electricity system is evolving to become more flexible, sustainable, and distributed. As the grid becomes smarter, so too do the tools that can exploit vulnerabilities in its digital backbone. Recently in IEEE Transactions on Smart Grid, Ismail et al. reported an electricity theft detection method using deep neural networks that can achieve 99.3% detection rates with less than 0.22% false positives.



中文翻译:

通过机器学习保护智能电网

电力系统正在发展,以变得更加灵活,可持续和分散。随着网格变得越来越智能,可以利用其数字主干网中的漏洞的工具也将变得更加智能。Ismail等人最近发表在《智能电网的IEEE Transactions》上。报告了使用深度神经网络的电盗窃检测方法,该方法可实现99.3%的检测率,且误报率低于0.22%。

更新日期:2020-03-18
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