EFedDSA: An Efficient Differential Privacy-Based Horizontal Federated Learning Approach for Smart Grid Dynamic Security Assessment

IF 3.7 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2023-07-13 DOI:10.1109/JETCAS.2023.3293253
Chao Ren;Tianjing Wang;Han Yu;Yan Xu;Zhao Yang Dong
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Abstract

Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) in smart cyber-physical grids has attracted significant research interest in recent years. However, the current centralized ML architectures have limited scalability, are vulnerable to privacy exposure, and are costly to manage. To resolve these limitations, we propose a novel effective and secure distributed DSA method based on horizontal federated learning (HFL) and differential privacy (DP), namely EFedDSA. It leverages local system operating data to predict and estimate the system stability status and optimize the power systems in a decentralized fashion. In order to preserve the privacy of the distributed DSA operating data, EFedDSA incorporates Gaussian mechanism into DP. To reduce the computational burden from multiple transmission communication rounds, a discounting method for the total communication round is proposed to reduce the total transmission rounds. Theoretical analysis on the Gaussian mechanism of EFedDSA provides formal DP guarantees. Extensive experiments conducted on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed EFedDSA method can achieve advantageous DSA performance with fewer communication rounds, while protecting the privacy of the local model information compared to the state of the art.
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EFedDSA:一种用于智能电网动态安全评估的高效基于差分隐私的水平联合学习方法
在机器学习(ML)技术的推动下,智能网络物理网格中的数据驱动动态安全评估(DSA)近年来引起了人们极大的研究兴趣。然而,当前集中式机器学习架构的可扩展性有限,容易受到隐私暴露的影响,并且管理成本很高。为了解决这些限制,我们提出了一种基于水平联邦学习(HFL)和差分隐私(DP)的新型高效安全的分布式数据分析方法,即EFedDSA。它利用本地系统运行数据来预测和估计系统的稳定状态,并以分散的方式优化电力系统。为了保护分布式DSA运行数据的隐私性,EFedDSA在DP中引入了高斯机制。为了减少多传输通信轮的计算负担,提出了一种总通信轮的折现方法,以减少总传输轮数。对EFedDSA的高斯机制进行理论分析,提供了正式的DP保证。在新英格兰10机39总线测试系统和综合伊利诺伊州49机200总线测试系统上进行的大量实验表明,所提出的EFedDSA方法能够以较少的通信轮数获得较好的DSA性能,同时保护了局部模型信息的隐私性。
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来源期刊
CiteScore
8.50
自引率
2.20%
发文量
86
期刊介绍: The IEEE Journal on Emerging and Selected Topics in Circuits and Systems is published quarterly and solicits, with particular emphasis on emerging areas, special issues on topics that cover the entire scope of the IEEE Circuits and Systems (CAS) Society, namely the theory, analysis, design, tools, and implementation of circuits and systems, spanning their theoretical foundations, applications, and architectures for signal and information processing.
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Introducing IEEE Collabratec Table of Contents IEEE Journal on Emerging and Selected Topics in Circuits and Systems Information for Authors IEEE Circuits and Systems Society Information IEEE Journal on Emerging and Selected Topics in Circuits and Systems Publication Information
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