CAREER:Probe-to-Learn Power Distribution Networks

该职业计划的目标是为配电网开发一个全面的数据分析框架，吸引学生参与令人兴奋的能源工程领域，并推进配电系统的教育。为了完成任何有意义的电网优化任务，配电系统运营商需要精确地了解每个节点的耗电量或发电量、线路和变压器参数以及电网的拓扑结构。然而，目前有限的仪器和它们的庞大规模使得配电网络在一般情况下无法观察到。为了补充智能计量和电网传感基础设施，本文介绍了一种新的电网探测技术。电网探测可以通过命令驻留在节点子集的智能逆变器来实现，这些逆变器有意地但即时地干扰它们的功率注入。启动网络化的物理系统并随后感知计量节点上产生的电压，可以揭示非计量负载和网络拓扑结构。将智能逆变器引入其预期控制功能之外的原始和潜在变革的想法开创了新的电网数据分析。这个程序超越了探测，并通过处理智能电表数据来绘制可观测性限制。现代优化方案及其实时和分散的变体将处理大规模不平衡配电网络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，该计划在于电力系统，非线性系统识别和统计学习的关系；提高我们对网格数据独特模式的理解；并发现了逆变器在电网监测和控制中的新用途。通过实现分布式可再生能源、电动汽车和客户参与的高效、可靠的整合，增强配电网的态势感知能力有利于社会。对行业的预期好处是尖端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增值。拟议的活动为网格数据处理带来了潜在的变革范例，与教育和推广目标巧妙地结合在一起。该项目通过实践学习活动和基于电网的协作应用程序游戏，面向大学预科女生。它还涉及本科生研究，并通过访问住宅社区继续向本科生推广。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 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.