Collaborative Research: Online Data Stream Fusion and Deep Learning for Virtual Meter in Smart Power Distribution Systems

随着信息和通信技术在工程系统中的不断部署，在线传输数据成为可能。在线学习算法可以利用这样的高价值数据来增强电网等国家关键基础设施的运行。与输电电网不同，配电系统缺乏通过传感基础设施进行广泛的直接在线测量。这使得对配电系统的准确监测成为一项具有挑战性的任务，对配电系统的可靠运行至关重要，特别是在大规模集成间歇性可再生能源的配电系统中，这增加了总负荷发电值的可变性。拟议的研究使得能够对大规模整合可再生能源的配电系统进行可靠的监测，这对公众产生了经济和社会影响。所提出的在线优化技术，将在这个项目中进行研究，可以应用于电力工程以外的数据流上的各种学习任务。将通过开发关于机器学习和智能电网的跨学科教育模块来整合研究和教学。智能电网技术将通过定义并为电力系统和计算机科学交叉的项目提供指导，在高三学生中得到推广。来自STEM中代表性不足群体的有才华的学生将通过华盛顿州立大学和爱荷华大学的导师工程项目积极参与该项目。在配电网的每个节点(可能包括数千个节点)安装新的传感器/仪表是一项昂贵且需要多年规划的任务。此外，在传感器/仪表可能出现故障或丢失的情况下，实现可靠的传感平台所需的传感器/仪表冗余无法通过如此稀缺的传感基础设施来实现。对于这个具有挑战性的现实世界问题，我们提出的解决方案是“虚拟仪表”形式的分析方法。建议的“虚拟仪表”不是一个实际的物理设备；相反，它是一个联合建模范例，它将数据驱动的模型和基于物理的模型在闭环环境中与在线双向交互融合在一起。我们提出了一类连贯的、整体的、可行的流处理和在线学习算法，具有可证明的质量保证和学习成本，从而实现了这样的在线交互，打造了联合建模框架。首先，我们将创建一类自组织数据融合算法，可以从异质数据流中挖掘和提取可靠的价值。其次，该项目将设计一类在线学习算法，包括在线深度学习，以估计虚拟测量结果。该项目的第三个主要贡献是，拟议的“虚拟仪表”关闭了在线环境下数据驱动模型和基于物理的模型之间的交互循环，创建了一个联合建模框架，以增强对配电系统的实时监控。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。

With ever growing deployment of information and communication technologies in engineering systems online steaming of data becomes available. Online learning algorithms can utilize such high value data to enhance operation of national critical infrastructures such as power grids. Power distribution systems, unlike transmission power grids, lack extensive direct online measurement through sensing infrastructures. This makes an accurate monitoring of power distribution systems, which is crucial for reliable operation of the system, a challenging task, specifically in power distribution systems with massive integration of intermittent renewable energy sources that increase the variability of the aggregated load-generation values. The proposed research enables reliable monitoring of power distribution systems with massive integration of renewable energy which has economic and social impacts on the public. The proposed online optimization techniques, which will be investigated in this project, can be applied to a variety of learning tasks over data streams beyond power engineering. Research and teaching will be integrated through development of interdisciplinary educational modules on machine learning and smart power grids. The smart grid technologies will be promoted among high school seniors by defining and providing mentorship for projects that intersect power systems and computer science. Talented students from under-represented groups in STEM will be actively engaged in the project through the Washington State University and University of Iowa mentorship engineering programs. Installing new sensors/meters at every node of the power distribution network, which may include thousands of nodes, is an expensive and a multi-year planning task. Also, the required sensors/meters redundancy for achieving reliable sensing platforms in facing possible failure or loss of sensors/meters cannot be fulfilled with such a scarce sensing infrastructure. Our proposed solution to this challenging real-world problem is analytical methodologies in the form of 'Virtual Meter'. The proposed "Virtual Meter" is not an actual physical device; rather it is a co-modeling paradigm that fuses data-driven and physics-based models in a closed loop setting with online bidirectional interactions. We propose a class of coherent, holistic, and feasible stream processing and online learning algorithms with provable quality guarantees and incur learning cost that enables such an online interaction, forging the co-modeling framework. First, we will create a class of ad-hoc data fusion algorithms that can exploit and extract reliable values from heterogeneous data streams. Second, the project will devise a class of online learning algorithms including online deep learning to estimate virtual measurements. The third major contribution of the project is that the proposed 'Virtual Meter' closes the loop of interactions between data-driven and physics-based models in an online setting creating a co-modeling framework to enhance the real-time monitoring of power distribution systems.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.