深度学习定义的电力电子转换器

IF 2.6 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
S. Madichetty, Machina Venkata Siva Prasad, Koduru Sriranga Suprabhath, Ramana Anchuri, Sukumar Mishra
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引用次数: 0

摘要

为实现可持续发展目标,能源和交通领域正在发生重大变革。网络物理系统(CPS)是工业 4.0 的一个组成部分,可实现实时监控、提高效率和生成数据。然而,它也带来了挑战和脆弱性,尤其是网络攻击的风险[1]。关键基础设施领域包括电网、运输系统、数据中心和医疗保健等。管理技术与关键基础设施之间的关系涉及复杂的监管和道德考量。过度依赖 CPS 可能会导致单点故障,即一个系统的中断会连累其他系统,这就强调了谨慎实施的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Defined Power Electronic Converters
In pursuit of achieving the sustainable development goals, significant transformations are underway in the energy and transportation sectors. Cyber-physical system (CPS), a component of Industry 4.0, enables real-time monitoring, efficiency enhancement, and data generation. However, it also introduces challenges and vulnerabilities, particularly the risk of cyberattacks [1]. Critical infrastructure sectors include power grids, transportation systems, data centers, and healthcare, among others. Managing the relationship between technology and critical infrastructure involves complex regulatory and ethical considerations. Overreliance on CPS can lead to single points of failure, where a disruption in one system can cascade into others, emphasizing the need for cautious implementation.
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来源期刊
IEEE Power Electronics Magazine
IEEE Power Electronics Magazine ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
2.80
自引率
4.30%
发文量
86
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