<![CDATA[The integration of energy distribution systems and telecommunication networks is crucial for improving the reliability, efficiency, and scalability of smart grids. However, challenges such as electromagnetic interference (EMI), latency, and fault tolerance complicate seamless operation. This study proposes a hybrid framework using MATLAB/Simulink to model and simulate energy distribution, real-time monitoring, and fault detection in high-voltage environments. The simulation framework consists of a high-voltage energy distribution network modeled with multiple buses, transformers, and distributed renewable energy sources. IoT-based sensors are strategically placed at critical nodes to collect real-time voltage and current data, which are transmitted via 5G communication protocols using the MQTT messaging standard. Fault detection is performed using an AI-driven Support Vector Machine (SVM) algorithm, trained with historical fault data to detect anomalies and classify fault types with high accuracy. The simulation environment integrates power flow analysis, real-time fault detection mechanisms, and communication latency assessment to evaluate system performance. Key findings demonstrate up to 92.8% energy efficiency with 60% renewable energy penetration, fault recovery times reduced to 35 ms through AI-based detection, and communication latency maintained below 15 ms for IoT-based monitoring. These results validate the proposed framework’s ability to address critical challenges in smart grids, including EMI mitigation, fault tolerance, and system scalability. This research bridges the gap between energy distribution and telecommunication systems, offering a scalable and sustainable solution for smart grid optimization.]]>
