The content discusses the vulnerability of power systems to cyber-attacks due to the transition to digital substations. It introduces a machine learning approach using artificial neural networks (ANN) to differentiate between system faults and cyber-attacks. The proposed method can identify fault types and locations accurately. Various ML models are trained using transient fault measurements and cyber-attack data on substations. The study highlights challenges in detecting faults accurately within power systems and emphasizes the need for adaptable solutions. The paper presents a detailed analysis of different ML models' performance under various scenarios, including single faults, N-1 contingency events, and simultaneous fault occurrences.
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by Kuchan Park,... kl. arxiv.org 03-08-2024
https://arxiv.org/pdf/2311.13488.pdfDybere Forespørgsler