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Power Line Monitoring and Predictive Maintenance Specifically in the Context of Nigeria

Bulus Stephen Kaka, Ohumu Peter Enahoro, Dalyop Stephen Choji

Abstract

Power line monitoring and predictive maintenance are pivotal in enhancing the reliability, efficiency, and sustainability of modern electrical grids. This article conducts a comprehensive review to explore technological advancements, challenges, and future directions in this critical area of energy infrastructure management. The methodology employed involves a systematic review of current literature from peer-reviewed articles, industry reports, and international standards. This approach synthesizes insights into the adoption of advanced technologies such as Internet of Things (IoT), artificial intelligence (AI), and big data analytics for proactive maintenance strategies. By analyzing diverse sources, the study provides a robust foundation for understanding the transformative potential of these technologies in optimizing grid operations The pressing need to improve grid reliability amidst increasing energy demand and the integration of renewable energy sources forms the core problem statement. Traditional reactive maintenance practices are inadequate in addressing the complexities of modern grids, necessitating a shift towards predictive maintenance models. Challenges such as data integration complexities, cybersecurity risks, regulatory frameworks, and sustainability imperatives underscore the urgency for innovative solutions. The conceptual framework encompasses technological innovations in IoT sensor networks for real-time monitoring, AI- driven predictive analytics for equipment failure prediction, and digital twins for simulation modeling. These advancements empower utilities to monitor asset health, predict failures proactively, and optimize maintenance schedules to minimize downtime and operational costs. The findings highlight the transformative impact of integrating advanced technologies into power line monitoring and maintenance practices. Future directions emphasize the need for policy support, industry collaborat

Keywords

Power line monitoringPredictive maintenanceTechnological advancements

References

Adesanya, A. O., Akinola, A. A., & Ogunniyi, G. S. (2020). The impact of IoT on the maintenance of the Nigerian electricity grid. Journal of Industrial Engineering International, 16(2), 331-344. doi: 10.1007/s40092-019-00355-6 Ahmad, I., Ahmad, W., Khan, Z. A., & Anwar, S. (2021). IoT and machine learning-based predictive maintenance system for smart grid. Sustainable Energy Technologies and Assessments, 44, 101206. doi: 10.1016/j.seta.2021.101206 Fang, X., Misra, S., Xue, G., & Yang, D. (2021). Cyber-physical security of IoT-based predictive maintenance for smart grid. IEEE Transactions on Industrial Informatics, 17(6), 4113-4121. doi: 10.1109/TII.2020.3034469 IEA (2020). World Energy Outlook 2020. International Energy Agency. Retrieved from https://www.iea.org/reports/world-energy-outlook-2020 Kusiak, A., Verstraete, T., & Zhao, N. (2019). Predictive maintenance of a power distribution system using machine learning. IEEE Transactions on Industrial Electronics, 66(11), 8769-8776. doi: 10.1109/TIE.2018.2882916 Lam, H., & Kremers, E. (2020). Digital twins for predictive maintenance in smart grid infrastructure. Computers in Industry, 122, doi: 10.1016/j.compind.2020.103289 Lam, H., & Kremers, E. (2020). Digital twins for predictive maintenance in smart grid infrastructure. Computers in Industry, 122, doi: 10.1016/j.compind.2020.103289 Ogunseye, O. O., Adewumi, A. O., & Chinyio, E. A. (2019). Application of predictive maintenance models in the Nigerian electricity distribution sector. Journal of Facilities Management, 17(3), 260-278. doi: 10.1108/JFM-09-2018-0068 Oladele, A. O., Olufemi, O. E., & Oni, O. A. (2022). The application of digital twin technology in predictive maintenance: A case study of Abuja Electricity Distribution Company. Computers, Materials & Continua, 71(1), 341-357. doi: 10.32604/cmc.2022.021051 Opara, U. L., Oludolapo, O., & Iroham, C. O. (2021). Data integration in smart grid predictive maintenance: A case study of the Nigerian electricity sector. International Journal of Scientific & Engineering Research, 12(3), 57-65. Oyedepo, S. O., & Oreko, R. O. (2019). A review of predictive maintenance as a supporting technology for smart grid. Heliyon, 5(6), e01827. doi: 10.1016/j.heliyon.2019.e01827 Vieira, J., Monteiro, V., Pereira, F., Silva, R., & Vale, Z. (2021). Enhancing electrical grid resilience with digital twins: A systematic review. Renewable and Sustainable Energy Reviews, 148, 111299. doi: 10.1016/j.rser.2021.111299 Yao, Y., Wen, Z., Zhong, R., Wang, Y., & Gao, Y. (2021). An AI-driven approach for fault diagnosis and prediction in power systems. Electric Power Systems Research, 190, doi: 10.1016/j.epsr.2020.106672 Zhang, Y., Chen, J., Wang, F., & Li, S. (2020). A survey on internet of things for smart grid systems: Advances, challenges, and opportunities. IEEE Access, 8, 144152-144171. doi: 10.1109/ACCESS.2020.3019188

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