Physics Guided Data-Driven Characterization of Anomalies in Power Electronic Systems

Kaustubh Bhatnagar, Subham S. Sahoo, F. Iov, F. Blaabjerg
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引用次数: 3

Abstract

The transition of conventional power system onto power electronics dominated grid (PEDG) has lead to amplified complexity in system-level control schemes to maintain reliability and operational stability. Considering the abundance of data in PEDG, machine learning (ML) schemes have emerged as a promising alternative. In this article, a physical guided data-driven approach using pattern recognition neural network (PRNN) is employed with semi-supervised learning. To distinguish between the faults and cyber-attacks without relying historical data scenarios. Finally, the results of proposed approach are discussed by utilizing ML tools.
电力电子系统异常的物理指导数据驱动表征
传统电力系统向电力电子主导电网(PEDG)的转变,导致系统级控制方案的复杂性增加,以保持可靠性和运行稳定性。考虑到PEDG中大量的数据,机器学习(ML)方案已经成为一个有前途的替代方案。在本文中,一个物理引导数据驱动的方法使用模式识别神经网络(PRNN)采用semi-supervised学习。区分故障和网络攻击而不依赖历史数据场景。最后,利用机器学习工具对所提方法的结果进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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