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Yang et al. Intell Robot 2024;4(4):406-21 I http://dx.doi.org/10.20517/ir.2024.24 Page 420

5. CONCLUSIONS
We address the challenge of TSA in the park-level energy internet by proposing the VNN-DM model, an
innovative detection framework that combines VNNs with CNN, specifically tailored for µPMU-based TSA
detection. ExperimentalresultsvalidatethatVNN-DMachieveshighaccuracyandrobustness, outperforming
benchmark models, and thus advancing the current state-of-the-art in TSA detection. Through comprehen-
sive analysis of µPMU data, we demonstrate the practical applicability of the model to maintaining reliable
state estimation under TSA conditions, effectively mitigating a critical vulnerability in the Energy Internet.
The unique architecture of VNN-DM, integrating vector processing and capsule networks, enables precise
extraction of multi-scale temporal features, representing a significant methodological advancement over tra-
ditional approaches. Future efforts will focus on deploying the model on edge devices, thereby extending its
applicability to more diverse and complex real-world scenarios. VNN-DM thus offers a deployable solution
for bolstering grid resilience against TSA, contributing to the secure evolution of smart grid infrastructure and
setting a new benchmark within this field.

DECLARATIONS
Acknowledgements
A part of the experiment results in this paper is simulated by SimuNPS.

Authors’ contributions
Implemented the methodologies presented and wrote the paper: Yang J
Developed the idea of the proposed framework: Shi F
Responsible for data collection and technical support: Li Y, Zhao Z
Managed and supervised the research project: Cui Q
All authors have revised the text and agreed to the published version of the manuscript.

Availability of data and materials
Not applicable.

Financial support and sponsorship
The work is supported by the National Natural Science Foundation of China (No. U23A20651)

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2024.

REFERENCES
1. Wu Z, Mao L, Li K, He S. Power distribution network state assessment technology based on unified computing resource pool. In: 2022
7th Asia Conference on Power and Electrical Engineering (ACPEE); 2022 Apr 15-17; Hangzhou, China. IEEE; 2022. pp. 931–7. DOI
2. Phadke AG, Bi T. Phasor measurement units, WAMS, and their applications in protection and control of power systems. J Mod Power
Syst Clean Energy 2018;6:619–9. DOI
3. Sexauer J, Javanbakht P, Mohagheghi S. Phasor measurement units for the distribution grid: Necessity and benefits. In: 2013 IEEE PES
innovative smart grid technologies conference (ISGT); 2013 Feb 24-27; Washington, USA. IEEE; 2013. p. 1–6. DOI
4. Xue AC, Xu FY, You HY, Xu J, Martin KE, Bi T. Robust parameter identification of distribution line based on micro PMU. Electr Power
Autom Equip 2019;39:1–7. DOI