This project develops an integrated framework of communications, computation and control for understanding wide-area power system performance in the face of unpredictable disturbances. The power system is chosen as a particularly challenging cyber physical system (CPS) due to its extreme dimension, geographic reach and high reliability requirements. The following tasks are studied in the proposed research: (a) a Partial Difference Equation (PdE) framework to model the impact of network topology on the power system stability; (b) the design of a communication network for CPS, based on the PdE modeling;(c) the design of a control system, which addresses the challenges such as fast response and resource constraints; (d) the design of a computing infrastructure, which addresses the computation for controlling the power network, in particular, the communication complexity for controlling the power network in both cases of one-snapshot computation and iterative computations; and (e) the test and evaluation for both small scale system models of several hundred buses and very large system models of ~50,000 buses. This work contributes to the broader understanding of CPS with high reliability requirements, particularly, critical infrastructures such as the power grid. Modern infrastructures are complex systems of communications and computation tied to the controls of the physical system. The proposed research contributes to improved reliability by addressing the propagation of disturbances and advancing the understanding of geographically distributed CPS. The PIs plan to open multiple courses on CPS related to the proposed research.

Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project develops new computer system infrastructure and algorithms for self-sustainable data-driven systems in the field. Research outcomes of the project include (a) a low-maintenance, environmentally-friendly hardware platform with solar energy harvesting and super capacitor-based energy storage, (b) virtualization software infrastructure for low-power nodes to enable inter-operability among distributed field nodes and from/to the data center, and (c) new image and data processing approaches for resource-adaptive fidelity adjustment and function partitioning. The synergy between the self-sustainable hardware, system software support, wireless communications management, and application data processing manifests through global coordination for quality-of-service, energy efficiency, and data privacy. In broader impacts, this project enables data-driven intelligence in the field for important physical system domains. Integration of the technologies involved is accomplished through real-world system deployment and experimentation, including an intelligent campus traffic and parking management system and collaborative work with industry collaborators. The results of this project will further enhance the technological competitiveness for US industries in key areas such as intelligent transportation. The education component includes cross-disciplinary curriculum enhancements and the development of a new instructional platform for realistic experiments with cyber-physical systems. Within the scope of this project, the PIs perform mentoring and outreach activities to recruit/retain women and minorities in science and engineering.

Energy infrastructure is a critical underpinning of modern society. To ensure its reliable operation, a nation-wide or continent-wide situational awareness system is essential to provide high-resolution understanding of the system dynamics such that proper actions can be taken in real-time in response to power system disturbances and to avoid cascading blackouts. The power grid represents a typical highly dynamic cyber-physical system (CPS). The ever-increasing complexity and scale in sensing and actuation, compounded by the limited knowledge of the accurate system state have resulted in major system failures, such as the massive power blackout of August 2003 and the most recent Arizona/California blackout of September 2011. Therefore, methods and tools for monitoring and control of these and other such dynamic systems at high resolution are vital to an emergent generation of tightly coupled, physically distributed CPS. This project employs the power grid as a target application and develops a high-resolution, ultra-wide-area situational awareness system that synergistically integrates sensing, processing, and actuation. First, from the sensing perspective, high resolution is reflected in both measurement accuracy and potential for dense spatial coverage. Wide area, precise, synchronized, and affordable sensing in voltage angle and frequency measurements for large-scale observation is sorely needed to observe system disturbances and capture critical changes in the power grid. The crucial innovation of this work is to make accurate frequency measurement from low voltage distribution systems through the wide deployment of Frequency Disturbance Recorders (FDRs). Second, from a data processing perspective, high resolution is reflected in finer-scale data analysis to reveal hidden information. In practical CPS, events seldom occur in an isolated fashion; cascading events are more common and realistic. A new conceptual framework is presented in the study of event analysis, referred to as event unmixing, where real-world events are considered a mixture of more than one constituent root event. This concept is a key enabler for the analysis of events to go beyond what are immediately detectable in the system. The event formation process is interpreted from a linear mixing perspective and innovative sparsity-constrained unmixing algorithms are presented for multiple event separation and spatial-temporal localization. Third, to discover the high-level spatial-temporal correlation among root events in real time, a descriptive language is developed to discover patterns on the spatial and temporal information of root events. This descriptive language allows embedding pattern descriptions on the desirable and undesirable interactions between events in the system, which will then be compiled into distributed runtime constructs to be executed in deployed systems. Fourth, from the actuation perspective, the system pushes the intelligence toward the lower level of the power grid allowing local devices to make decisions and to react quickly to contingencies based on the high-resolution understanding of the system state, enabling a more direct reconfiguration of the physical makeup of the grid. Finally, the methods and tools are implemented and validated on an existing wide-area power grid monitoring system, the North American frequency monitoring network (FNET). Escalating demands for electricity coupled with an outdated power transmission grid pose a serious threat to the US economy. The transformative nature of this research is to turn a large volume of real-time data into actionable information and help prevent potential outages from happening. The power grid is a typical example of dynamic cyber physical system. Providing high-resolution situational awareness for the power grid has a direct and immediate impact on this and other CPS. The research is coupled with a strong educational component including active recruitment of students from underrepresented groups supported by existing programs and broad dissemination of research findings.

This project is to develop a dynamical systems model of distributed computation motivated from recent work on the distributed computation of averages. The key idea is that static optimization problems (particularly convex optimization problems) can be solved by designing a dynamic system that stabilizes around the optimal solution of the problem. Moreover, when the optimization problem is separable, then the designed dynamic system decomposes into a set of locally-interacting dynamic systems. This is expected to open a door to a host of new computational approaches that take advantage of recent developments in control engineering including robust control (providing a mechanism for errors introduced by discretization), Markovian Jump Linear Systems (providing a mechanism for random discretization time), event-driven control (providing a mechanism for assured asynchronous execution), control over networks (providing a mechanism for improved performance of distributed computational systems in general). The new approach is essential in emerging applications, where the optimization runs on physically separated agents, operating in a noisy environment and communicating over unreliable channels. As a test bed, the project will make use of a two-vehicle robotic system developed by the PI designed to monitor a crop of corn plants, where the dynamic systems perspective of this grant will, for example, allow for distributed optimal estimation toward the goal of optimal station-keeping. By studying how natural systems can collectively compute and optimize, this research has potential to impact many disciplines involving networked systems, from controlling the electric power grid, to modeling the behavior of social, biological or economic systems. It is directly applicable to cooperative networked multi-agent systems like robotic search and rescue missions and disaster-relief operations, distributed machine learning problems, and intelligent systems. An intriguing mix of motivating applications and theoretical problems offer a unique multidisciplinary educational opportunity to students who will be involved in the project, and provide exciting innovative material for courses and labs. Software developed will be distributed as open source via the CPS Virtual Organization.

This project explores balancing performance considerations and power consumption in cyber-physical systems, through algorithms that switch among different modes of operation (e.g., low-power/high-power, on/off, or mobile/static) in response to environmental conditions. The main theoretical contribution is a computational, hybrid optimal control framework that is connected to a number of relevant target applications where physical modeling, control design, and software architectures all constitute important components. The fundamental research in this program advances state-of-the-art along four different dimensions, namely (1) real-time, hybrid optimal control algorithms for power management, (2) power-management in mobile sensor networks, (3) distributed power-aware architectures for infrastructure management, and (4) power-management in embedded multi-core processors. The expected outcome, which is to enable low-power devices to be deployed in a more effective manner, has implications on a number of application domains, including distributed sensor and communication networks, and intelligent and efficient buildings. The team represents both a research university (Georgia Institute of Technology) and an undergraduate teaching university (York College of Pennsylvania) in order to ensure that the educational components are far-reaching and cut across traditional educational boundaries. The project involves novel, inductive-based learning modules, where graduate students team with undergraduate researchers.