An IoT-Enabled Edge Intelligence Framework for Real-Time Fault Monitoring in Power Distribution Networks

Abstract

To ensure reliable operation of the distribution system, feeder faults, transformer stress and abnormal operating conditions need to be detected in a timely fashion. This paper proposes an edge-intelligent monitoring and real-time communication system for a three-phase 11 kV/415/230 V power distribution system, which is based on IoT. The proposed system combines the sensing of the voltage, current, active power and the LT-side and transformer-side temperature sensors with local decision logic based on thresholds and remote communication through a dedicated mobile phone and control-room interface. A mathematical model was derived to explain the following: phase voltage, phase current, power utilization, voltage deviation, overcurrent, imbalance and temperature state. The system was tested using scenarios in MATLAB/Simulink representing various operating conditions such as overloading, undervoltage, overvoltage, overheating, line-fault and normal operating conditions. The simulation results showed that the monitoring logic maintained a normal state correctly without triggering false alarms, generated alarm at warning stage when progressive overload occurred, triggered alarm at undervoltage and over-voltage at 8.0 s, and generated the alarm in the line-fault state when the voltage collapsed in combination with current surge, and triggered the alarm at warning state during the overheating simulation. Under the line fault condition, the maximum current was increased to 203.43A as compared with 94.48A under normal condition, and the overheating condition reached 98.34 °C. The results show that the integration of electrical and thermal monitoring with edge-level decision logic leads to a better situation awareness and quicker operator response. The proposed framework provides a scalable smart distribution monitoring platform and a future enhancement path towards implementation with hardware, event logging, fault-location techniques and machine-learning based fault classification.