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彻底的变革。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，包括一种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，重点研究如何在网络数据攻击下获得低可观察性配电网中更好的状态估计。该奖学金将为博士生和未被充分代表的本科生提供获得宝贵经验和发展新技能的绝佳机会。PI将把新技术带回美国堪萨斯州立大学（KSU），并介绍给相关领域的其他研究者。该奖学金将促进KSU和NREL之间强有力的伙伴关系，并帮助堪萨斯州更好地实现其可再生能源目标。由于传感器数量有限，传统配电系统的可观测性非常低。由于网络的巨大规模和测量的有限可用性，获得所需的态势感知目前是不可行的。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察性，但使用信息的另一个日益增长的担忧是它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？美国国家可再生能源实验室（NREL）的培训和合作研究。培训和研究将侧重于开发和验证网络数据攻击下低可观测分布系统的新型状态估计方法。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)为可扩展性设计一个完全分布式的解决方案；Iii)为配电网络中的虚假数据注入攻击建模；iv)接受大规模基础设施联合仿真（HELICS）平台分层引擎培训；v)执行概念验证。该项目将扩大PI？并将他的职业道路转向一个有前途的方向，即加强电网的网络安全。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配电系统的态势感知。该项目的研究成果将整合到PI的课程中，以帮助留住现有的STEM学生。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.