Design and Implementation of Digital Protection Systems for High-Voltage Transmission Lines and Power Transformers
DOI:
https://doi.org/10.60087/jklst.vol4.n4.015Keywords:
High-Voltage Transmission, Digital Protection Systems, Smart Grid, System Reliability, Power TransformersAbstract
Background: High-voltage transmission lines and power transformers need dependable protection systems which maintain their operational status to deliver continuous power distribution. Methods: The research used a quantitative cross-sectional survey design which gathered data from 250 power sector employees who worked as engineers and technicians and operators. The research employed descriptive statistics together with Pearson's correlation and multiple regression and ANOVA to study how people understood things and how they performed and used what they learned. The research team used Cronbach's alpha to evaluate instrument reliability for their study. Results: People view fast fault detection at 22 % and reduced downtime at 20 % and improved accuracy at 18 % as their main advantages. The main obstacles for system implementation consist of expensive startup expenses which affect 24 % of cases and complex technical requirements that impact 20 % and insufficient trained staff members who represent 18 percent. The evaluation of performance demonstrates that fault detection speed has improved by 20 % and reliability has improved by 19 %. The analysis shows that people who understand a system tend to adopt it more (r = 0.45) and those who receive training develop better implementation skills (r = 0.41) but the cost factor creates a negative impact on adoption rates (r = −0.38). Conclusion: Digital protection systems provide power systems with better performance and enhanced reliability through their operation. Strategic investment along with capacity building and supportive policies need to exist for power systems to achieve successful implementation and modernization.
Downloads
References
Aung, M. M., & Chang, Y. S. (2013). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172–184. https://doi.org/10.1016/j.foodcont.2013.11.007
Barros, R. M., Da Costa, E. G., Araujo, J. F., De Andrade, F. L., & Ferreira, T. V. (2019). Contribution of inrush current to mechanical failure of power transformers windings. High Voltage, 4(4), 300–307. https://doi.org/10.1049/hve.2018.5019
Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in Industry, 101, 1–12. https://doi.org/10.1016/j.compind.2018.04.015
Caena, F., & Redecker, C. (2019). Aligning teacher competence frameworks to 21st century challenges: The case for the European Digital Competence Framework for Educators (Digcompedu). European Journal of Education, 54(3), 356–369. https://doi.org/10.1111/ejed.12345
Dul, J., Bruder, R., Buckle, P., Carayon, P., Falzon, P., Marras, W. S., Wilson, J. R., & Van Der Doelen, B. (2012). A strategy for human factors/ergonomics: developing the discipline and profession. Ergonomics, 55(4), 377–395. https://doi.org/10.1080/00140139.2012.661087
Elgenedy, M., Ahmed, K., Burt, G., Rogerson, G., & Jones, G. (2021). Unlocking the UK continental shelf electrification Potential for offshore oil and gas installations: A power grid architecture perspective. Energies, 14(21), 7096. https://doi.org/10.3390/en14217096
Eltawil, M. A., & Zhao, Z. (2009). Grid-connected photovoltaic power systems: Technical and potential problems—A review. Renewable and Sustainable Energy Reviews, 14(1), 112–129. https://doi.org/10.1016/j.rser.2009.07.015
Faiz, J., & Siahkolah, B. (2010). Solid-state tap-changer of transformers: Design, control and implementation. International Journal of Electrical Power & Energy Systems, 33(2), 210–218. https://doi.org/10.1016/j.ijepes.2010.08.016
Friedman, B., Kahn, P. H., Borning, A., & Huldtgren, A. (2013). Value sensitive design and information systems. In Philosophy of engineering and technology (pp. 55–95). https://doi.org/10.1007/978-94-007-7844-3_4
Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450
Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241–265. https://doi.org/10.1016/j.ymssp.2017.11.024
Lee, E. (2015). The Past, Present and Future of Cyber-Physical Systems: A focus on models. Sensors, 15(3), 4837–4869. https://doi.org/10.3390/s150304837
Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging Digital twin technology in Model-Based Systems engineering. Systems, 7(1), 7. https://doi.org/10.3390/systems7010007
Majka, Ł., & Klimas, M. (2019). Diagnostic approach in assessment of a ferroresonant circuit. Electrical Engineering, 101(1), 149–164. https://doi.org/10.1007/s00202-019-00761-5
Maza-Ortega, J. M., Mauricio, J. M., Barragán-Villarejo, M., Demoulias, C., & Gómez-Expósito, A. (2019). Ancillary services in hybrid AC/DC low voltage distribution networks. Energies, 12(19), 3591. https://doi.org/10.3390/en12193591
Menachemi, N., & Collum. (2011). Benefits and drawbacks of electronic health record systems. Risk Management and Healthcare Policy, 4, 47. https://doi.org/10.2147/rmhp.s12985
Opana, S., & Chang, C. (2020). Mitigation of current transformer saturation on medium voltage switchgears in APR1400 of Nuclear power plants. IEEJ Transactions on Electrical and Electronic Engineering, 15(11), 1630–1640. https://doi.org/10.1002/tee.23233
Peek, S. T., Wouters, E. J., Van Hoof, J., Luijkx, K. G., Boeije, H. R., & Vrijhoef, H. J. (2014). Factors influencing acceptance of technology for aging in place: A systematic review. International Journal of Medical Informatics, 83(4), 235–248. https://doi.org/10.1016/j.ijmedinf.2014.01.004
Sachs, J. D., Schmidt-Traub, G., Mazzucato, M., Messner, D., Nakicenovic, N., & Rockström, J. (2019). Six Transformations to achieve the Sustainable Development Goals. Nature Sustainability, 2(9), 805–814. https://doi.org/10.1038/s41893-019-0352-9
Schwartz, G., Tee, B. C., Mei, J., Appleton, A. L., Kim, D. H., Wang, H., & Bao, Z. (2013). Flexible polymer transistors with high pressure sensitivity for application in electronic skin and health monitoring. Nature Communications, 4(1), 1859. https://doi.org/10.1038/ncomms2832
Shao, K. (2011). A novel method of transformer fault diagnosis based on extension theory and information fusion in wireless sensor networks. Energy Procedia, 12, 669–678. https://doi.org/10.1016/j.egypro.2011.10.091
Shepherd, M., Turner, J. A., Small, B., & Wheeler, D. (2018). Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’ revolution. Journal of the Science of Food and Agriculture, 100(14), 5083–5092. https://doi.org/10.1002/jsfa.9346
Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D. (2021). Digital Twin: origin to future. Applied System Innovation, 4(2), 36. https://doi.org/10.3390/asi4020036
Sobouti, M. A., Azizian, D., Bigdeli, M., & Gharehpetian, G. B. (2019). Electromagnetic transients modelling of split‐winding traction transformers for frequency response analysis. IET Science Measurement & Technology, 13(9), 1362–1371. https://doi.org/10.1049/iet-smt.2019.0164
Sorlie, P. D., Avilés-Santa, L. M., Wassertheil-Smoller, S., Kaplan, R. C., Daviglus, M. L., Giachello, A. L., Schneiderman, N., Raij, L., Talavera, G., Allison, M., LaVange, L., Chambless, L. E., & Heiss, G. (2010). Design and Implementation of the Hispanic Community Health Study/Study of Latinos. Annals of Epidemiology, 20(8), 629–641. https://doi.org/10.1016/j.annepidem.2010.03.015
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. Npj Digital Medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2017). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9–12), 3563–3576. https://doi.org/10.1007/s00170-017-0233-1
Ustundag, A., & Cevikcan, E. (2017). Industry 4.0: Managing the digital transformation. In Springer series in advanced manufacturing. https://doi.org/10.1007/978-3-319-57870-5
Zeadally, S., Hunt, R., Chen, Y., Irwin, A., & Hassan, A. (2010). Vehicular ad hoc networks (VANETS): status, results, and challenges. Telecommunication Systems, 50(4), 217–241. https://doi.org/10.1007/s11235-010-9400-5
Zhang, P., White, J., Schmidt, D. C., Lenz, G., & Rosenbloom, S. T. (2018). FHIRChain: Applying blockchain to securely and scalably share clinical data. Computational and Structural Biotechnology Journal, 16, 267–278. https://doi.org/10.1016/j.csbj.2018.07.004
Zhang, S., Teizer, J., Lee, J., Eastman, C. M., & Venugopal, M. (2012). Building Information Modeling (BIM) and Safety: automatic safety checking of construction models and schedules. Automation in Construction, 29, 183–195. https://doi.org/10.1016/j.autcon.2012.05.006
Zissis, D., & Lekkas, D. (2010). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583–592. https://doi.org/10.1016/j.future.2010.12.006
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)

This work is licensed under a Creative Commons Attribution 4.0 International License.
©2024 All rights reserved by the respective authors and JKLST.



