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Yang et al. Intell Robot 2024;4(4):406-21 Intelligence & Robotics
DOI: 10.20517/ir.2024.24

Research Article Open Access

VNN-DM: a vector neural network-based detection model

for time synchronization attacks in park-level energy
internet

1
1
1
1
Jiacheng Yang , Fanrong Shi , Yunlong Li , Zhihang Zhao , Qiushi Cui 2
1 School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China.
2 School of Electrical Engineering, Chongqing University, Chongqing 400044, China.
Correspondence to: Dr. Fanrong Shi, School of Information Engineering, Southwest University of Science and Technology, No.59,
Middle Section of Qinglong Avenue, Fucheng District, Mianyang 621010, China. E-mail: sfr_swust@swust.edu.cn
How to cite this article: Yang J, Shi F, Li Y, Zhao Z, Cui Q. VNN-DM: a vector neural network-based detection model for time
synchronization attacks in park-level energy internet. Intell Robot 2024;4(4):406-21. http://dx.doi.org/10.20517/ir.2024.24

Received: 15 Oct 2024 First Decision: 6 Nov 2024 Revised: 17 Nov 2024 Accepted: 21 Nov 2024 Published: 29 Nov 2024
Academic Editor: Simon Yang Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Micro phasor measurement units (mPMUs) 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 mPMU 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 mPMU 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 mPMU
measurement data demonstrate that the model exhibits excellent detection capacity in various performance
metrics, underscoring its precision and robustness.

Keywords: Time synchronization attack, vector neural networks, mPMUs, park-level energy internet, attack detection

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
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