CAREER:Probe-to-Learn Power Distribution Networks

该职业建议的目标是为配电网开发一个全面的数据分析框架，让学生参与令人兴奋的能源工程领域，并推进配电系统教育。为了完成任何有意义的电网范围的优化任务，配电系统运营商将需要精确地知道在每个节点处消耗或产生的功率、线路和Transformer参数以及电网的拓扑结构。然而，目前，有限的仪器及其庞大的规模使得配电网一般无法观察到。为了补充智能计量和电网传感基础设施，这里介绍了电网探测的新技术。电网探测可以通过命令驻留在节点子集处的智能逆变器有意地但瞬时地扰动它们的功率注入来实现。启动网络化的物理系统并随后感测计量节点处产生的电压可以揭示非计量负载和网络拓扑。在其预期控制功能之外使用智能逆变器的原始和潜在的变革性想法开创了新的电网数据分析。该程序超越了探测，并通过处理智能电表数据来绘制可观测性极限。当代优化方案及其实时和分散的变体将处理在大规模不平衡配电网中收集的流和空间不完整的电压和功率注入数据。在更广泛的影响上，该程序位于电力系统，非线性系统识别和统计学习的联系中;推进我们对电网数据中独特模式的理解;并发现了逆变器在电网监测和控制中的新用途。提高配电网的态势感知能力可以使分布式可再生能源、电动汽车和客户参与的有效和可靠的集成受益于社会。对行业的预期好处是先进的电网分析解决方案，先进的资产管理以及逆变器和智能电表的附加值。拟议的活动为网格数据处理带来了一种潜在的变革模式，与教育和外联目标巧妙地结合在一起。该计划通过动手学习活动和基于应用程序的电网协作游戏，接触到大学前的女学生。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。

The goal of this CAREER proposal is to develop a comprehensive data analytics framework for distribution grids, engage students in the exciting field of energy engineering, and advance education on distribution systems. To accomplish any meaningful grid-wide optimization task, the distribution system operator will need to precisely know the power consumed or generated at every node, the line and transformer parameters, and the topology of the grid. However currently, limited instrumentation and their sheer size render power distribution grids unobservable in general. To complement the smart metering and grid sensing infrastructure, the novel technique of grid probing is introduced here. Grid probing can be accomplished by commanding smart inverters residing at a subset of nodes to intentionally yet instantaneously perturb their power injections. Actuating the networked physical system and subsequently sensing the incurred voltages at metered nodes can unveil non-metered loads and network topologies. The original and potentially transformative idea of engaging smart inverters outside their intended control functionality pioneers new grid data analytics. This program goes beyond probing and maps the observability limits by processing smart meter data too. Contemporary optimization schemes together with their real-time and decentralized variants will deal with streaming and spatially incomplete voltage and power injection data collected at large-scale unbalanced distribution networks.On the broader impact, this program lies at the nexus of power systems, nonlinear system identification, and statistical learning; advances our understanding of unique patterns in grid data; and discovers novel uses for inverters in grid monitoring and control. Enhancing situational awareness in distribution grids benefits society by enabling the efficient and reliable integration of distributed renewable energy, electric vehicles, and customer participation. The expected benefits to industry are cutting-edge grid analytics solutions, advanced asset management, and value added for inverters and smart meters. The proposed activities bring about a potentially transformative paradigm for grid data processing, neatly integrated with educational and outreach objectives. The program reaches pre-college female students through hands-on learning activities and collaborative app-based games on electric grids. It also involves undergraduate research and continues our outreach to undergraduate students through visits to residential communities.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.