Privacy-Preserving Real-Time Smart Grid Topology Analysis: A Graph Neural Networks Approach

Abstract

The transition to smart grids represents a significant evolution in power system technology, incorporating advanced communication and control mechanisms to enhance efficiency, reliability, and sustainability. Real-time topology analysis is a critical functionality in smart grids, enabling the detection of faults, optimization of operations, and maintenance of grid stability. However, this analysis presents challenges, including the need for efficient data processing, handling dynamic topologies, and preserving data privacy. This article proposes a novel approach for real-time smart grid topology analysis using graph neural networks (GNNs) with integrated privacy-preserving techniques. The GNN model is designed to capture complex relationships within the grid, facilitating accurate node classification, edge prediction, and anomaly detection. To address privacy concerns, we incorporate differential privacy and secure multi-party computation, ensuring that sensitive data remains protected during analysis. Extensive experiments conducted on a synthetic smart grid dataset demonstrate the effectiveness of the proposed method, achieving high accuracy, precision, recall, and AUC-ROC scores across various tasks. A case study further illustrates the practical applicability of our approach, showing efficient real-time performance and robust privacy guarantees.