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

Washington State University, Pullman WA

Investigators

Abstract

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.