Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach

TL;DR

This paper introduces a graph diffusion-based framework for robust sensor placement in cyber-physical power systems, improving anomaly detection and communication resilience through an innovative algorithm.

Contribution

It proposes the EFGD algorithm utilizing graph diffusion, cross-entropy, and experience feedback to optimize sensor placement in CPPS, addressing NP-hard challenges.

Findings

EFGD enhances convergence by 18.9% over existing methods.

Average reward improves by 22.90% over DDPO.

System robustness and reliability are significantly increased.

Abstract

With advancements in physical power systems and network technologies, integrated Cyber-Physical Power Systems (CPPS) have significantly enhanced system monitoring and control efficiency and reliability. This integration, however, introduces complex challenges in designing coherent CPPS, particularly as few studies concurrently address the deployment of physical layers and communication connections in the cyber layer. This paper addresses these challenges by proposing a framework for robust sensor placement to optimize anomaly detection in the physical layer and enhance communication resilience in the cyber layer. We model the CPPS as an interdependent network via a graph, allowing for simultaneous consideration of both layers. Then, we adopt the Log-normal Shadowing Path Loss (LNSPL) model to ensure reliable data transmission. Additionally, we leverage the Fiedler value to measure graph resilience against line failures and three anomaly detectors to fortify system safety. However, the optimization problem is NP-hard. Therefore, we introduce the Experience Feedback Graph Diffusion (EFGD) algorithm, which utilizes a diffusion process to generate optimal sensor placement strategies. This algorithm incorporates cross-entropy gradient and experience feedback mechanisms to expedite convergence and generate higher reward strategies. Extensive simulations demonstrate that the EFGD algorithm enhances model convergence by 18.9% over existing graph diffusion methods and improves average reward by 22.90% compared to Denoising Diffusion Policy Optimization (DDPO) and 19.57% compared to Graph Diffusion Policy Optimization (GDPO), thereby significantly bolstering the robustness and reliability of CPPS operations.