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