SafePowerGraph-HIL: Real-Time HIL Validation of Heterogeneous GNNs for Bridging Sim-to-Real Gap in Power Grids

TL;DR

This paper presents SafePowerGraph-HIL, a framework combining hardware-in-the-loop simulations with heterogeneous GNNs to validate and improve real-time power grid state estimation, bridging the sim-to-real gap.

Contribution

It introduces a novel HIL validation framework using Hypersim and cloud-based HGNN models for power system analysis, enhancing real-time robustness and accuracy.

Findings

High-fidelity data generation via HIL on IEEE 9-bus system

Real-time data transmission and storage in cloud database

Validated HGNN model accurately predicts system states under varied conditions

Abstract

As machine learning (ML) techniques gain prominence in power system research, validating these methods' effectiveness under real-world conditions requires real-time hardware-in-the-loop (HIL) simulations. HIL simulation platforms enable the integration of computational models with physical devices, allowing rigorous testing across diverse scenarios critical to system resilience and reliability. In this study, we develop a SafePowerGraph-HIL framework that utilizes HIL simulations on the IEEE 9-bus system, modeled in Hypersim, to generate high-fidelity data, which is then transmitted in real-time via SCADA to an AWS cloud database before being input into a Heterogeneous Graph Neural Network (HGNN) model designed for power system state estimation and dynamic analysis. By leveraging Hypersim's capabilities, we simulate complex grid interactions, providing a robust dataset that captures critical parameters for HGNN training. The trained HGNN is subsequently validated using newly generated data under varied system conditions, demonstrating accuracy and robustness in predicting power system states. The results underscore the potential of integrating HIL with advanced neural network architectures to enhance the real-time operational capabilities of power systems. This approach represents a significant advancement toward the development of intelligent, adaptive control strategies that support the robustness and resilience of evolving power grids.