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- W4385934426 abstract "Cybersecurity of smart grids have been a topic of much interest in recent years. As this critical infrastructure operation increases dependency on automated processes and controls, exposure to cyber-physical threats become inevitable. Considering cyber-physical security of the grid, much focus of attention has been made towards smart grids real-time monitoring solutions, including the state estimation process. Analyzing the relevant literature, one can note though that seldom research has been done on cyber-physical security of smart grids protection systems. Protection systems have intangible value towards grid reliability. This paper presents a cybersecurity framework for smart grids protection systems. A physics-based inspired machine learning solution is at the core process of the framework. Processed relay inputs and outputs are used by a deep predictive coding network. A formal mathematical model, a quasi-static state estimator, provides an oracle when low confidence decision is reached. Evolving knowledge is derived through reinforcement learning. Implementation aspects considering the Pacific Northwest National Laboratory Electricity Infrastructure Operations Center are presented. Built as an extra control layer to protection systems, without hard-to-derive parameters, highlights potential aspects towards real-life applications." @default.
- W4385934426 created "2023-08-18" @default.
- W4385934426 creator A5010754368 @default.
- W4385934426 creator A5013263487 @default.
- W4385934426 date "2023-04-27" @default.
- W4385934426 modified "2023-10-14" @default.
- W4385934426 title "Cyber-Physical Power Systems Protection: The Byzantine Cybersecurity Framework" @default.
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- W4385934426 doi "https://doi.org/10.1109/kpec58008.2023.10215452" @default.
- W4385934426 hasPublicationYear "2023" @default.
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