Abstract
In this era of Web World-3, usage of the Internet of Things (IoT) in power system networks has attained scalable altitude. By using this technology, data can be transferred and monitored from the far end. Further, the suggested corrective actions are also executed. However, recently, many cyber-attacks have been observed on the power system in which activities such as injection of false data, unwanted switching operations, formation of sub-system layers, and false indication of failure of cyber-physical components (CpC) are experienced. In the worst case, it leads to invite cascade tripping of the power system. Utmost care is taken for the selection of CpC for the power system. However, it is observed that the cyber attackers mostly take entry into the network using the CpC of the power system. Cyber attackers perform false switching operations and false data injection. This article suggests a cyber security concept to minimize the false switching operations and false data injection in the power system network. A hardware model is prepared and the working scheme is implemented using IoT technology. The hardware result suggests that the un-authentic attempt/cyber-attack has been identified and the alarm is generated. It also does not permit the un-authentic person to access the real-time data. A wide range of applicability of the suggested scheme has been verified by hardware results. In addition to this, the power factor correction algorithm also works satisfactorily in the hardware along with the cyber security constraints. In order to prove wide range of applicability of the suggested scheme, additional features such as controlling of multiple switching devices and interlock between CB and earthing switch has been successfully implemented in developed hardware.
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interest: The authors state no conflict of interest.
-
Research funding: None declared.
-
Data availability: Not applicable.
Hardware data.
Apparatus | Specifications |
---|---|
Relay unit | Operating voltage 5 V |
Microcontroller | Arduino uno micro controller, Node MCU |
Load | Lamp load and choke coil rated 230 V |
Breadboard unit | 5 V, 3.3 V DC |
References
1. Saleem, Y, Crespi, N, Rehmani, MH, Copeland, R. Internet of Things-aided smart grid: technologies, architectures, applications, prototypes, and future research directions. IEEE Access 2019;7:62962–3003. https://doi.org/10.1109/access.2019.2913984.Search in Google Scholar
2. Agriesti, S, Gandini, P, Marchionni, G, Paglino, V, Ponti, M, Studer, L. Evaluation approach for a combined implementation of day 1 C-ITS and truck platooning. In: IEEE vehicular technology conference; 2018. pp. 1–6. https://doi.org/10.1109/vtcspring.2018.8417876.Search in Google Scholar
3. Fortino, G, Member, S, Savaglio, C. Internet of Things as system of systems: a review of methodologies. Frameworks Platforms Tools 2021;51:223–36.10.1109/TSMC.2020.3042898Search in Google Scholar
4. Orlando, M, Member, GS, Estebsari, A, Member, S, Pons, E, Pau, M, et al.. A smart meter infrastructure for smart grid IoT applications. IEEE Internet Things J 2022;9(14):12529–41, https://doi.org/10.1109/jiot.2021.3137596.Search in Google Scholar
5. Li, W, Member, GS, Logenthiran, T, Member, S, Homes, AS. A novel smart energy theft system (SETS) for IoT-based smart home. IEEE Internet Things J 2019;6(3):5531–9.10.1109/JIOT.2019.2903281Search in Google Scholar
6. Stauffer, J, Zhang, Q. s2Cloud: a novel cloud-based precision health system for smart and secure IoT big data harnessing. Discov Internet Things 2024;4. https://doi.org/10.1007/s43926-024-00055-8.Search in Google Scholar
7. Punia, R, Malik, A, Singh, S. Innovative image interpolation based reversible data hiding for secure communication. Discov Internet Things 2023;3. https://doi.org/10.1007/s43926-023-00050-5.Search in Google Scholar
8. Saleem, MU, Usman, MA, Usman, MR, Politis, C. Design, deployment and performance evaluation of an IoT based smart energy management system for demand side management in smart grid. IEEE Access 2022;10:15261–78. https://doi.org/10.1109/access.2022.3147484.Search in Google Scholar
9. Fortino, G, Member, S, Russo, W, Savaglio, C, Member, S. Agent-oriented cooperative smart objects: from IoT system design to implementation. IEEE Trans Syst Man Cybern Syst 2018;48(11):1939–56, https://doi.org/10.1109/tsmc.2017.2780618.Search in Google Scholar
10. Bento, AC, Hurtado, CV, Gatti, DC, Solís Garza, CM, Fadul, DO, Camacho-Leon, S. Practical results for IoT virtual classes with Arduino IoT cloud. In: 2023 IEEE CHILEAN conference on electrical, electronics engineering, information and communication technologies (CHILECON). IEEE; 2023. pp. 1–6.10.1109/CHILECON60335.2023.10418658Search in Google Scholar
11. Konda, D, Patel, U, Rathi, R, Shah, J, Chothani, N. Improving protection of compensated transmission line using IoT enabled adaptive auto reclosing scheme. Int J Emerg Elec Power Syst 2023;1–10. https://doi.org/10.1515/ijeeps-2023-0074.Search in Google Scholar
12. Tuballa, ML, Abundo, ML. A review of the development of smart grid technologies. Renew Sustain Energy Rev 2016;59:710–25. https://doi.org/10.1016/j.rser.2016.01.011.Search in Google Scholar
13. IEEE. IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces sponsored by the IEEE standard for interconnection and interoperability of distributed energy resources with associate; 2018. 137 p.Search in Google Scholar
14. Falvo, MC, Martirano, L, Sbordone, D, Bocci, E. Technologies for smart grids: a brief review. In: 12th International conference on environment and electrical engineering, EEEIC 2013; 2013. pp. 369–75.10.1109/EEEIC.2013.6549544Search in Google Scholar
15. Zahedi, A. Developing a system model for future smart grid. In: Innovative smart grid technologies Asia (ISGT), 2011 IEEE PES; 2011. pp. 1–5.10.1109/ISGT-Asia.2011.6167326Search in Google Scholar
16. Tatar, U, Bahsi, H, Gheorghe, A. Impact assessment of cyber attacks: a quantification study on power generation systems. In: 2016 11th systems of systems engineering conference, SoSE 2016; 2016. pp. 1–6.10.1109/SYSOSE.2016.7542959Search in Google Scholar
17. Xu, Y. A review of cyber security risks of power systems: from static to dynamic false data attacks. Protect Control Mod Power Syst 2020;5. https://doi.org/10.1186/s41601-020-00164-w.Search in Google Scholar
18. Shukla, A, Dutta, S, Sahu, SK, Sadhu, PK. A narrative perspective of island detection methods under the lens of cyber-attack in data-driven smart grid. J Electr Syst Inf Technols 2023;10:14. https://doi.org/10.1186/s43067-023-00083-4.Search in Google Scholar
19. Gao, Y, Member, S, Li, S, Member, S, Xiao, Y, Dong, W, et al.. An iterative optimization and learning-based IoT system for energy management of connected buildings. IEEE Internet Things J 2022;9(21):21246–59, https://doi.org/10.1109/jiot.2022.3176306.Search in Google Scholar
20. Oruc, A, Chowdhury, N, Gkioulos, V. A modular cyber security training programme for the maritime domain. Int J Inf Secur 2024;23:1477–512. https://doi.org/10.1007/s10207-023-00799-4.Search in Google Scholar
21. Rajkumar, VS, Tealane, M, Stefanov, A, Presekal, A, Palensky, P. Cyber attacks on power system automation and protection and impact analysis. In: 2020 IEEE PES innovative smart grid technologies Europe (ISGT-Europe). IEEE; 2020:247–54 pp.10.1109/ISGT-Europe47291.2020.9248840Search in Google Scholar
22. Jacinto, E, Martinez, F, Martinez, F. Enhanced device-specific encryption for IoT: leveraging microcontroller UIDs and dedicated cryptographic hardware. WSEAS Trans Syst Control [Internet]. 2024;19:177–84. https://doi.org/10.37394/23203.2024.19.19. Available from: https://wseas.com/journals/sac/2024/a385103-015(2024).pdf.Search in Google Scholar
23. Nawi, MNM, Selvi, T, Neeraja, P, Yellapragada, RK, Jain, H. An IoT assimilated distributed control method for green electrical transmission grids. WSEAS Trans Power Syst 2023;18:321–9. https://doi.org/10.37394/232016.2023.18.33.Search in Google Scholar
24. Cendoya, MG, Verne, SA, Valla, MI, Battaiotto, PE. Wind energy conversion system with integrated power smoothing capability based on an EVT-coupled flywheel. WSEAS Trans Power Syst 2024;19:68–78. https://doi.org/10.37394/232016.2024.19.9.Search in Google Scholar
25. Suryono, W, Setiyo Prabowo, A, Suhanto, M’ti, Sazali, A. Monitoring and controlling electricity consumption using Wemos D1 Mini and smartphone. IOP Conf Ser Mater Sci Eng 2020;909:012014. https://doi.org/10.1088/1757-899x/909/1/012014.Search in Google Scholar
26. Koch, G. Programming the Arduino. In: The LEGO Arduino Cookbook, 1st ed. Berkeley, CA: Apress; 2020:25–36 pp.10.1007/978-1-4842-6303-7_3Search in Google Scholar
27. Fielding, R, Nottingham, M, Reschke, J, editors. HTTP semantics; 2022.10.17487/RFC9110Search in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston