VNN-DM: a vector neural network-based detection model for time synchronization attacks in park-level energy internet Article Swipe
Jiacheng Yang
,
Fanrong Shi
,
Yu Li
,
Zhisheng Zhao
,
Qiushi Cui
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20517/ir.2024.24
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20517/ir.2024.24
Micro phasor measurement units (µPMUs) provide high-precision voltage and current phasor data, allowing real-time state estimation and fault detection, which are critical for the stability and reliability of modern power systems. However, their reliance on accurate time synchronization makes them vulnerable to time synchronization attacks (TSAs), which can disrupt grid monitoring and control by corrupting µPMU data. Addressing these vulnerabilities is essential to ensure the secure and resilient operation of smart grids and energy internet technologies. To address these challenges, intelligent detection methods are essential. Therefore, this paper proposes a µPMU measurement data TSA detection model based on vector neural networks (VNNs). This model initially employs a vector neural network to process raw data, effectively extracting and analyzing temporal features. During the same time, a capsule network is employed to classify these temporal features. On this basis, a reconstruction network is used to verify the representational capacity of the model. Simulations based on µPMU measurement data demonstrate that the model exhibits excellent detection capacity in various performance metrics, underscoring its precision and robustness.
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Metadata
- Type
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- Language
- en
- Landing Page
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- OA Status
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- OpenAlex ID
- https://openalex.org/W4404885331
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404885331Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20517/ir.2024.24Digital Object Identifier
- Title
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VNN-DM: a vector neural network-based detection model for time synchronization attacks in park-level energy internetWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-11-29Full publication date if available
- Authors
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Jiacheng Yang, Fanrong Shi, Yu Li, Zhisheng Zhao, Qiushi CuiList of authors in order
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https://doi.org/10.20517/ir.2024.24Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.20517/ir.2024.24Direct OA link when available
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The Internet, Artificial neural network, Computer science, Energy (signal processing), Artificial intelligence, Synchronization (alternating current), Machine learning, Real-time computing, Computer network, World Wide Web, Statistics, Mathematics, Channel (broadcasting)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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