RII Track-4: Robust Matrix Completion State Estimation in Low-Observability Distribution Systems under False Data Injection Attacks

RII Track-4：虚假数据注入攻击下低可观测性分布系统中的鲁棒矩阵完成状态估计

• 批准号: 1929147
• 负责人: Hongyu Wu
• 金额: $ 19.87万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929147&HistoricalAwards=false
• 关键词: RII; Track; Robust; Matrix; Completion

The operational landscape at electric distribution grids is undergoing a radical transformation. Notably, the impact of distributed renewable energy sources and the impetus to improve cybersecurity are challenging the status quo and calling for innovative techniques to enhance situational awareness in the distribution grid. With the support of an EPSCoR Research Fellowship, the PI and a Ph.D. student will receive training on new techniques, including a novel state estimation approach and a next-generation cyber-physical system simulation platform, at the National Renewable Energy Laboratory (NREL). The PI and the student will closely collaborate with NREL researchers by focusing on how to acquire better state estimation in low-observability distribution grids under cyber data attacks. This fellowship will provide an excellent opportunity for a Ph.D. student and an underrepresented undergraduate to gain valuable experience and develop new skillsets. The PI will bring the new techniques back to the home institution, i.e., Kansas State University (KSU), and introduce them to other investigators in related fields. This fellowship will foster a strong partnership between KSU and NREL, and help the state of Kansas better meet its renewable energy goals.Legacy distribution systems traditionally have very low observability due to a limited amount of sensors. Obtaining required situational awareness is currently not feasible due to the immense scale of the network and limited availability of measurements. While the use of information from advanced metering infrastructure and phasor measurement units can improve the observability of distribution systems, another growing concern with the use of information is its susceptibility to cyber data attacks. The overarching goal of this fellowship is to support the PI?s training and collaborative research at the National Renewable Energy Laboratory (NREL). The training and research will focus on the development and validation of a novel state estimation approach in the low-observability distribution system under cyber data attacks. Specific training and research objectives include: i) developing a bad data detection approach based on matrix completion state estimation; ii) designing a fully distributed solution for scalability; iii) modeling false data injection attacks in distribution networks; iv) receiving training on Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS) platform; and v) performing proof-of-concept validation. This project will expand the PI?s research capacity and transform his career path towards a promising direction in enhancing cybersecurity of the power grid. This project will result in novel tools that can be used by system operators to enhance the situational awareness of distribution systems. The research outcome of this project will be integrated into the PI's courses to aid the retention of current STEM students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

配电网的运营格局正在发生根本性的转变。值得注意的是，分布式可再生能源的影响和改善网络安全的动力正在挑战现状，并呼吁采用创新技术来增强配电网的态势感知能力。在EPSCoR研究奖学金的支持下，PI和博士学生将在国家可再生能源实验室（NREL）接受新技术培训，包括新的状态估计方法和下一代网络物理系统仿真平台。PI和学生将与NREL研究人员密切合作，专注于如何在网络数据攻击下在低可观测性配电网中获得更好的状态估计。这个奖学金将提供一个很好的机会，博士。学生和代表性不足的本科生获得宝贵的经验和发展新的技能。主要研究者将把新技术带回本国机构，即，堪萨斯州立大学（KSU），并将他们介绍给相关领域的其他研究人员。该奖学金将促进KSU和NREL之间的强有力的合作伙伴关系，并帮助堪萨斯州更好地实现其可再生能源目标。传统的配电系统由于传感器数量有限，可观测性非常低。由于网络规模巨大，测量数据有限，目前无法获得所需的态势感知。虽然使用来自先进计量基础设施和相量测量单元的信息可以提高配电系统的可观测性，但使用信息的另一个日益关注的问题是其对网络数据攻击的敏感性。这个奖学金的首要目标是支持PI？在国家可再生能源实验室（NREL）的培训和合作研究。培训和研究的重点是在网络数据攻击下的低可观测性配电系统中开发和验证一种新的状态估计方法。具体的培训和研究目标包括：i）开发一种基于矩阵完备状态估计的不良数据检测方法; ii）设计一种完全分布式的可扩展性解决方案; iii）对配电网络中的虚假数据注入攻击进行建模; iv）接受大规模基础设施协同仿真分层引擎（HELICS）平台的培训;以及v）执行概念验证。这个项目将扩大PI？的研究能力，并将他的职业道路转向加强电网网络安全的有前途的方向。该项目将产生新的工具，可用于系统运营商，以提高配电系统的态势感知。该项目的研究成果将被整合到PI的课程中，以帮助现有STEM学生的保留。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Optimal D-FACTS Placement in Moving Target Defense Against False Data Injection Attacks

• DOI: 10.1109/tsg.2020.2977207
• 发表时间: 2020-09
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Bo Liu;Hongyu Wu

Net Load Redistribution Attacks on Nodal Voltage Magnitude Estimation in AC Distribution Networks

• DOI: 10.1109/isgt-europe47291.2020.9248915
• 发表时间: 2020-05
• 期刊: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)
• 作者: Hang Zhang;Bo Liu;Hongyu Wu

A Fast Penalty-Based Gauss-Seidel Method for Stochastic Unit Commitment With Uncertain Load and Wind Generation

• DOI: 10.1109/oajpe.2021.3079150
• 发表时间: 2021
• 期刊: IEEE Open Access Journal of Power and Energy
• 影响因子: 3.8
• 作者: Ananth M. Palani;Hongyu Wu;M. Morcos

Matrix-Completion-Based False Data Injection Attacks Against Machine Learning Detectors

• DOI: 10.1109/tsg.2023.3308339
• 发表时间: 2024-03
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Bo Liu;Hongyu Wu;Qihui Yang;Hang Zhang;Yajing Liu;Y. Zhang

Systematic planning of moving target defence for maximising detection effectiveness against false data injection attacks in smart grid

• DOI: 10.1049/cps2.12012
• 发表时间: 2021-05
• 期刊: IET Cyper-Phys. Syst.: Theory & Appl.
• 作者: Bo Liu;Hongyu Wu