AI-Enabled Intelligent O&M for Telecom Power Systems

Abstract

The integration of artificial intelligence into telecommunications power system operations and maintenance (O&M) represents a paradigm shift from traditional preventive maintenance to predictive and self-healing management models. This paper provides a systematic analysis of AI-empowered intelligent O&M frameworks specifically designed for telecom power infrastructure, which forms the critical backbone of network reliability. We examine key implementation architectures combining IoT-based multi-sensor data acquisition, cloud-edge computing platforms, and AI-driven analytical engines for real-time equipment health monitoring and anomaly detection. The study demonstrates through telecom carrier case studies how machine learning algorithms—particularly long short-term memory networks and graph neural networks—can accurately predict battery degradation, detect grid instability patterns, and optimize energy dispatch across heterogeneous power assets. Our findings reveal that AI implementation reduces operational costs by 25-40% through deferred capital expenditure and lower field dispatch frequency, while improving system availability to 99.99% through early fault detection and automated response mechanisms. However, significant implementation challenges persist, including data siloing across legacy systems, model interpretability requirements for mission-critical systems, and cybersecurity vulnerabilities introduced through increased connectivity. The research concludes that successful intelligent O&M implementation requires not only technological integration but also organizational adaptation, including new skill development programs and updated operational protocols. This comprehensive analysis provides both a architectural framework and implementation roadmap for telecom operators navigating the transition to AI-driven power infrastructure management.