EPCN: Learning power grids from limited measurements: fundamental limits and practical algorithms

该项目开发了使未来电网管理更加高效，安全和强大的方法。未来能源系统的智能基础和实用算法的发展是一个重要的挑战和本项目的目标。具体来说，网络拓扑结构和线路参数是电力系统管理的关键信息。即使这些信息中的错误会显著影响系统操作，但在识别它们方面的工作相对较少，特别是当测量在时间和空间上都受到限制时。这个问题在中压和低压配电网中更加严重，不仅因为今天缺乏监控基础设施，而且因为拓扑结构可能会更频繁地响应负载或太阳能发电的变化。我们的目标是发展一个理论和算法的拓扑和线路参数识别。我们将专注于测量的情况下，只有在有限的位置或/和有限的时间段。这些设置是困难的，但现实的，因为，例如，目前，大多数配电系统具有来自配电网与大容量输电网接口的变电站的测量，以及来自终端用户处的智能仪表的测量，但是在这两者之间没有太多的测量。未来的应用程序可能必须基于有限数量的样本近实时地做出识别决策。拟议的研究包括三个推力。Thrust 1（有限空间测量的学习）侧重于当并非所有网络节点都是可观察的时的理论和算法，但是在必须进行估计之前，可以随着时间的推移从可观察节点收集足够的样本。推力2（有限时间测量的学习）关注的是所有网络节点都是可观察的，但只有有限数量的样本可用于识别的情况。目标3（具有有限测量的集成学习）将目标1和目标2中开发的理论和算法集成到一个整体识别系统中，该系统适用于测量在空间和时间上都受到限制的情况。 该提案中开发的方法适用于其他网络系统（社交，通信，交通，金融）。 该项目将研究与教育相结合，包括课程开发和与创业精神的联系。该奖项反映了NSF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。

This project develops methods to make the management of future power grids more efficient, secure, and robust. The development of an intellectual basis and practical algorithms for future energy systems is an important challenge and a goal of this project.Specifically, network topology and line parameters are critical information for the management of power systems. Even though errors in such information can significantly impact system operation, there is relatively little work in identifying them, especially when measurements are limited both temporally and spatially. This problem is even more severe in medium-voltage and low-voltage distribution grids, not only because the lack of monitoring infrastructure today, but also because topology may change more frequently in response to changes in load or solar generation. Our goal is to develop a theory and algorithms for topology and line parameter identification.We will focus on cases where measurements are available only at limited locations or/and for a limited time period. These settings are difficult but realistic, as, e.g., most distribution systems today have measurements from substations where a distribution grid interfaces with the bulk transmission grid, and from smart meters at end users, but not much measurements in between. Future applications may have to make identification decisions in near real-time based on limited numbers of samples. The proposed research consists of three thrusts. Thrust 1 (Learning with limited spatial measurements) focuses on the theory and algorithms when not all network nodes are observable, but sufficient samples can be collected over time from the observable nodes before an estimation has to be made. Thrust 2 (Learning with limited temporal measurements) focuses on the case where all network nodes are observable, but only a limited number of samples are available for identification. Thrust 3 (Integrated learning with limited measurements) integrates the theory and algorithms developed in Thrusts 1 and 2 into an overall identification system that is applicable where measurements are limited both spatially and temporally. The methods developed in the proposal are applicable to other network systems (social, communications, transportation, financial). The project integrates research with education including curriculum development and linking to entrepreneurship.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.