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

配电网的运营格局正在经历一场根本性的变革。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，并要求采用创新技术来增强配电网的态势感知。在EPSCoR研究奖学金的支持下，PI和一名博士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模拟平台。PI和这名学生将与NREL的研究人员密切合作，专注于如何在网络数据攻击下的低可观测性配电网中获得更好的状态估计。这一奖学金将为博士生和代表不足的本科生提供一个极好的机会，让他们获得宝贵的经验和发展新的技能。PI将把新技术带回国内机构，即堪萨斯州立大学(KSU)，并将它们介绍给相关领域的其他研究人员。这一奖学金将促进KSU和NREL之间的牢固合作伙伴关系，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，传统上可观测性非常低。由于网络的巨大规模和可获得的测量有限，目前无法获得所需的态势感知。虽然使用先进的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令人担忧的问题是它容易受到网络数据攻击。该奖学金的首要目标是支持皮？S在国家可再生能源实验室的培训和合作研究。培训和研究将集中在网络数据攻击下的低可观测性分布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)开发基于矩阵完成状态估计的不良数据检测方法；ii)设计可伸缩性的全分布式解决方案；iii)对配电网中的虚假数据注入攻击进行建模；iv)接受针对大规模基础设施协同仿真(HELICS)平台的分层引擎的培训；v)执行概念验证。该项目将扩大皮？S的研究能力，使他的职业道路朝着加强电网网络安全的方向发展。该项目将产生新的工具，系统操作员可以使用这些工具来增强对配电系统的态势认识。这个项目的研究成果将被整合到PI的课程中，以帮助留住现有的STEM学生。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.