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

随着工程系统中信息和通信技术的不断发展，在线数据处理成为可能。在线学习算法可以利用这些高价值数据来增强电网等国家关键基础设施的运行。与输电网不同，配电系统缺乏通过传感基础设施进行的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，这对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，这会增加总负载产生值的可变性。拟议的研究能够可靠地监测大规模集成可再生能源的配电系统，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本项目中进行研究，可以应用于电力工程以外的数据流上的各种学习任务。通过开发机器学习和智能电网的跨学科教育模块，将研究和教学结合起来。智能电网技术将通过为电力系统和计算机科学交叉的项目定义和提供指导，在高中高年级学生中推广。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指导工程项目积极参与该项目。在配电网络的每个节点（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，在面对可能出现的传感器/仪表故障或丢失时，实现可靠传感平台所需的传感器/仪表冗余无法在如此稀缺的传感基础设施中实现。我们提出的这个具有挑战性的现实问题的解决方案是“虚拟仪表”形式的分析方法。拟议的“虚拟仪表”不是一个实际的物理设备；相反，它是一种协同建模范式，将数据驱动和基于物理的模型融合在一个闭环设置中，并进行在线双向交互。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，这些算法具有可证明的质量保证，并产生学习成本，从而实现了这种在线交互，锻造了协同建模框架。首先，我们将创建一类ad-hoc数据融合算法，它可以从异构数据流中挖掘和提取可靠的值。其次，该项目将设计一类在线学习算法，包括在线深度学习来估计虚拟测量。该项目的第三个主要贡献是，拟议的“虚拟电表”在在线设置中关闭了数据驱动模型和基于物理模型之间的交互循环，创建了一个共同建模框架，以增强对配电系统的实时监控。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。

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.