Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning

IF 6.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Ramzi Al-Sharawi;Abdelfatah Ali;Mostafa Shaaban;Nasser Qaddoumi;Mohamed S. Abdalzaher
{"title":"Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning","authors":"Ramzi Al-Sharawi;Abdelfatah Ali;Mostafa Shaaban;Nasser Qaddoumi;Mohamed S. Abdalzaher","doi":"10.1109/OJCOMS.2025.3564069","DOIUrl":null,"url":null,"abstract":"Smart grid cybersecurity is a critical research challenge due to society’s dependence on reliable electricity. Existing research primarily addresses cybersecurity by focusing on the optimal placement of phasor measurement units (PMUs) to ensure topological observability and minimize system costs, followed by developing AI-based attack detection algorithms. However, these studies fail to simultaneously consider system cost, loss in system observability, and false data injection attack (FDIA) detection performance. Thus, this paper proposes a novel approach by formulating this issue as a tri-objective functions optimization problem. The proposed approach optimizes PMU allocation to maximize topological observability and minimize system cost while improving the FDIA detection performance using machine learning. Specifically, the k-Nearest Neighbors (KNN) model’s Brier loss is used as an objective function within the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization framework to represent the FDIA detection performance. To demonstrate the proposed approach’s efficacy, it is tested on the IEEE 38-bus distribution system. To verify the strength of the developed KNN classifier, we examined it using seven different metrics: accuracy, brier loss, F1-score, elapsed time, learning curve, receiver operating characteristic curve (ROC) curve, and confusion matrix. The simulation results show that the KNN model achieved superior attack classification performance with a top accuracy of 99.99% and a minimal Brier loss of <inline-formula> <tex-math>$9.9478 \\times 10^{-4}$ </tex-math></inline-formula> on the ±0.2% PMU observation tolerance dataset. These results highlight the success of our framework in concurrently optimizing attack detection performance, topological observability, and system cost.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"6 ","pages":"4036-4050"},"PeriodicalIF":6.3000,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975827","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10975827/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Smart grid cybersecurity is a critical research challenge due to society’s dependence on reliable electricity. Existing research primarily addresses cybersecurity by focusing on the optimal placement of phasor measurement units (PMUs) to ensure topological observability and minimize system costs, followed by developing AI-based attack detection algorithms. However, these studies fail to simultaneously consider system cost, loss in system observability, and false data injection attack (FDIA) detection performance. Thus, this paper proposes a novel approach by formulating this issue as a tri-objective functions optimization problem. The proposed approach optimizes PMU allocation to maximize topological observability and minimize system cost while improving the FDIA detection performance using machine learning. Specifically, the k-Nearest Neighbors (KNN) model’s Brier loss is used as an objective function within the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization framework to represent the FDIA detection performance. To demonstrate the proposed approach’s efficacy, it is tested on the IEEE 38-bus distribution system. To verify the strength of the developed KNN classifier, we examined it using seven different metrics: accuracy, brier loss, F1-score, elapsed time, learning curve, receiver operating characteristic curve (ROC) curve, and confusion matrix. The simulation results show that the KNN model achieved superior attack classification performance with a top accuracy of 99.99% and a minimal Brier loss of $9.9478 \times 10^{-4}$ on the ±0.2% PMU observation tolerance dataset. These results highlight the success of our framework in concurrently optimizing attack detection performance, topological observability, and system cost.
结合机器学习解决智能电网相量测量单元最优放置和攻击检测问题
由于社会对可靠电力的依赖,智能电网网络安全是一项关键的研究挑战。现有的研究主要通过关注相量测量单元(pmu)的最佳位置来解决网络安全问题,以确保拓扑可观察性并最大限度地降低系统成本,其次是开发基于人工智能的攻击检测算法。然而,这些研究没有同时考虑系统成本、系统可观察性损失和虚假数据注入攻击(FDIA)检测性能。因此,本文提出了一种新的方法,将该问题表述为三目标函数优化问题。该方法优化PMU分配,以最大化拓扑可观察性和最小化系统成本,同时利用机器学习提高FDIA检测性能。具体而言,在非支配排序遗传算法II (NSGA-II)优化框架中,使用k-近邻(KNN)模型的Brier损失作为目标函数来表示FDIA检测性能。为了验证该方法的有效性,在IEEE 38总线配电系统上进行了测试。为了验证所开发的KNN分类器的强度,我们使用七个不同的指标来检查它:准确性,brier损失,f1分数,运行时间,学习曲线,接收者工作特征曲线(ROC)曲线和混淆矩阵。仿真结果表明,KNN模型在±0.2% PMU观测容差数据集上取得了优异的攻击分类性能,最高准确率达到99.99%,最小Brier损失为$9.9478 \ × 10^{-4}$。这些结果突出了我们的框架在同时优化攻击检测性能、拓扑可观察性和系统成本方面的成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
13.70
自引率
3.80%
发文量
94
审稿时长
10 weeks
期刊介绍: The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023. The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include: Systems and network architecture, control and management Protocols, software, and middleware Quality of service, reliability, and security Modulation, detection, coding, and signaling Switching and routing Mobile and portable communications Terminals and other end-user devices Networks for content distribution and distributed computing Communications-based distributed resources control.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信