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.

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.

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.

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.

5. Zhao T, Li Z, Zou B, He Z, Ren S. Wide-area time synchronization method for mutual preparation of satellite clock and network clock. Automat Electr Power Syst 2017;41:202-7.

6. Xu J, Wu Z, Hu Q, et al. Interval state estimation for active distribution networks considering uncertainties of multiple types of DGs and loads. Proc CSEE 2018;38:3255-66.

7. Tian S, Li K, Wei S, Fu Y, Li Z, Liu S. Security situation awareness approach for distribution network based on synchronous phasor measurement unit. Proc CSEE 2021;41:617-31.

8. Qian B, Cai Z, Xiao Y, et al. Review on time synchronization attack in power system. Power Syst Technol 2020;44:4035-45.

9. Zhao X, Liu G, Li L. Importance-driven denial-of-service attack strategy design against remote state estimation in multi-agent intelligent power systems. Intell Robot 2024;4:244-55.

10. Bi T, Guo J, Xu K, Zhang L, Yang Q. The impact of time synchronization deviation on the performance of synchrophasor measurements and wide area damping control. IEEE Trans Smart Grid 2017;8:1545-52.

11. Shang Y. Resilient vector consensus over random dynamic networks under mobile malicious attacks. Comput J 2023;67:1076-86.

12. Shang Y. Resilient consensus in continuous-time networks with $$\ell$$-hop communication and time delay. Syst Control Lett 2023;175:105509.

13. Zeng H, Ye Z, Zhang D, Lu Q. Robust distributed model predictive control of connected vehicle platoon against DoS attacks. Intell Robot 2023;3:288-305.

14. Xue A, Xu F, Martin KE, Xu J, You H, Bi T. Linear approximations for the influence of phasor angle difference errors on line parameter calculation. IEEE Trans Power Syst 2019;34:3455-64.

15. Saraswat D, Bhattacharya P, Zuhair M, Verma A, Kumar A. AnSMart: a SVM-based anomaly detection scheme via system profiling in smart grids. In: 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM); 2021 Apr 28-30; London, UK. IEEE; 2021. pp. 417–22.

16. Niu X, Li J, Sun J, Tomsovic K. Dynamic detection of false data injection attack in smart grid using deep learning. In: 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT); 2019 Feb 18-21; Washington, USA. IEEE; 2019. pp. 1–6.

17. Jeyaselvi M, Sathya M, Suchitra S, Ibrahim SJA, Chakravarthy NSK. SVM-based cloning and jamming attack detection in IoT sensor networks. In: Goar V, Kuri M, Kumar R, Senjyu T, editors. Advances in information communication technology and computing: proceedings of AICTC 2021. Springer; 2022. pp. 461–71.

18. Zou Z. Research on detection and defense in smart grid GPS spoofing attack. 2022.

19. Zhang Y, Wang J, Chen B. Detecting false data injection attacks in smart grids: a semi-supervised deep learning approach. IEEE Trans Smart Grid 2020;12:623-34.

20. Tan Q. GAN-based adversarial attack and defense on time series classification. 2023.

21. Hoang TM, Nguyen NM, Duong TQ. Detection of eavesdropping attack in UAV-aided wireless systems: Unsupervised learning with one-class SVM and k-means clustering. IEEE Wireless Commun Lett 2019;9:139-42.

22. Ma Z, Ma H, Gao X, et al. An improved DDoS attack detection model based on unsupervised learning in smart grid. In: Xiong J, Wu S, Peng C, Tian Y, editors. International Conference on Mobile Multimedia Communications. Springer; 2021. pp. 550–62.

23. Zheng C, Wang H, Liu R. A review of research on DDoS attack detection in SDNs. Comput Eng Appl 2024;1–20. Available from: https://link.cnki.net/urlid/11.2127.TP.20240814.1351.006. [Last accessed on 25 Nov 2024].

24. Huang R, Li Y. False phasor data detection under time synchronization attacks: a neural network approach. IEEE Trans Smart Grid 2022;13:4828-36.

25. Akkaya I, Lee EA, Derler P. Model-based evaluation of GPS spoofing attacks on power grid sensors. In: 2013 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES); 2013 May 20; Berkeley, USA. IEEE; 2013. p. 1–6.

26. Zhang Z, Gong S, Dimitrovski AD, Li H. Time synchronization attack in smart grid: impact and analysis. IEEE Trans Smart Grid 2013;4:87-98.

27. Jiang X, Zhang J, Harding BJ, Makela JJ, Domínguez-García AD. Spoofing GPS receiver clock offset of phasor measurement units. IEEE Trans Power Syst 2013;28:3253-62.

28. Shepard DP, Humphreys TE, Fansler AA. Evaluation of the vulnerability of phasor measurement units to GPS spoofing attacks. Int J Crit Infrastruct Prot 2012;5:146-53.

29. Shereen E, Delcourt M, Barreto S, Dán G, Le Boudec JY, Paolone M. Feasibility of time-synchronization attacks against PMU-based state estimation. IEEE Trans Instrum Meas 2020;69:3412-27.

30. Hinton GE, Krizhevsky A, Wang SD. Transforming auto-encoders. In: Artificial Neural Networks and Machine Learning - ICANN 2011: 21st International Conference on Artificial Neural Networks. Springer; 2011. pp. 44–51.

31. Sabour S, Frosst N, Hinton GE. Dynamic routing between capsules. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017. pp. 3859-69.