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

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Wangyong Guo, Siyang Shao, Kexin Zhang, Rongqiang Feng, Xueqiong Wu, Zhihao Zhang
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引用次数: 0

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.

保护隐私的实时智能电网拓扑分析:一种图神经网络方法
向智能电网的过渡代表了电力系统技术的重大演变,结合了先进的通信和控制机制,以提高效率、可靠性和可持续性。实时拓扑分析是智能电网的一项关键功能,可以实现故障检测、运行优化和电网稳定维护。然而,这种分析提出了一些挑战,包括需要有效的数据处理、处理动态拓扑和保护数据隐私。本文提出了一种利用集成隐私保护技术的图神经网络(gnn)进行实时智能电网拓扑分析的新方法。GNN模型旨在捕获网格内的复杂关系,促进准确的节点分类、边缘预测和异常检测。为了解决隐私问题,我们结合了差分隐私和安全多方计算,确保在分析过程中敏感数据得到保护。在合成智能电网数据集上进行的大量实验证明了所提出方法的有效性,在各种任务中实现了较高的准确性、精密度、召回率和AUC-ROC分数。一个案例研究进一步说明了我们的方法的实际适用性,显示了高效的实时性能和健壮的隐私保证。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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