Real-Time State Estimation in Smart Grids over 5G Networks: Experimental Validation Using Raspberry Pis and Typhoon HIL

2026-06-25

AI Summary

This study addresses the gap between simulation-based research and real-world deployment in smart grid state estimation by presenting an experimental validation over a commercial 5G network. The authors develop a multi-node testbed integrating Raspberry Pi edge nodes with Typhoon hardware-in-the-loop (HIL) simulation, implementing end-to-end real-time state estimation and fault detection using an IEEE 4-bus feeder model, a phasor data concentrator (PDC), and key performance indicators (KPIs). Experimental results demonstrate that 5G achieves an average end-to-end latency approximately 6.5 times lower than LTE Cat-M, maintains high estimation accuracy under both steady-state and dynamic conditions, and enables fault detection with a latency as low as 0.80 seconds. This work provides the first empirical evidence of the real-time capability and reliability of smart grid state awareness in an operational 5G environment, effectively bridging the gap between simulation and practical implementation.

Abstract

Reliable, low-latency communication is critical for real-time monitoring and control in modern Smart Grids (SGs). The emergence of 5G networks, with enhanced reliability, significantly lower latency, and native support for massive machine-type communication, offers strong potential to enable advanced grid applications such as state estimation (SE) and fault detection. While existing studies investigate 5G for SG use cases, most rely on simulations or analytical models; experimental validation using real hardware and SG data remains limited. This paper fills this gap by presenting a fully experimental validation of real-time SE over a commercial 5G network using a 5G-based multi-node testbed built with Raspberry Pi (RPi)-based SG nodes and a Typhoon Hardware-in-the-Loop (HIL) real-time simulator. We first characterize 5G communication performance using simulated SG data under varying reporting rates and deployment environments by evaluating Key Performance Indicators (KPIs) such as end-to-end delay, jitter, and frame loss. Experimental results show that the worst-case mean delay observed for the 5G is approximately 6.5x lower than that of our previous LTE cat-M study at the corresponding reporting rate. We then stream real-time voltage, current, and phase-angle measurements-generated by an IEEE 4-node feeder model in Typhoon HIL simulator-to a remote Phasor Data Concentrator (PDC) for SE and fault detection. Results demonstrate that 5G-enabled measurements support accurate SE under both steady-state and dynamic load variations. Furthermore, fault-detection experiments confirm reliable and prompt fault detection, with detection delays as low as 0.80 s.

Problem

• Smart Grids
• 5G Networks
• State Estimation
• Experimental Validation
• Real-Time Monitoring

Innovation

• 5G networks
• real-time state estimation
• Hardware-in-the-Loop (HIL)
• Smart Grids
• experimental validation