Harnessing wind energy is one of the pressing challenges of our time. The scale, complexity, and robustness of wind power systems present compelling cyber-physical system design issues. Leveraging the physical infrastructure at Purdue, this project aims to develop comprehensive computational infrastructure for distributed real-time control. In contrast to traditional efforts that focus on programming-in-the-small, this project emphasizes programmability, robustness, longevity, and assurance of integrated wind farms. The design of the proposed computational infrastructure is motivated by, and validated on, complex cyber-physical interactions underlying Wind Power Engineering. There are currently no high-level tools for expressing coordinated behavior of wind farms. Using the proposed cyber-physical system, the project aims to validate the thesis that integrated control techniques can significantly improve performance, reduce downtime, improve predictability of maintenance, and enhance safety in operational environments. The project has significant broader impact. Wind energy in the US is the fastest growing source of clean, renewable domestically produced energy. Improvements in productivity and longevity of this clean energy source, even by a few percentage points will have significant impact on the overall energy landscape and decision-making. Mitigating failures and enhancing safety will go a long way towards shaping popular perceptions of wind farms -- accelerating broader acceptance within local communities. Given the relative infancy of "smart" wind farms, the potential of the project cannot be overstated.

The national transmission networks that deliver high voltage electric power underpin our society and are central to the ongoing transformation of the American energy infrastructure. Transmission networks are very large and complicated engineering systems, and "keeping the lights on" as the transformation of the American energy infrastructure proceeds is a fundamental engineering challenge involving both the physical aspects of the equipment and the cyber aspects of the controls, communications, and computers that run the system. The project develops new principles of cyber-physical engineering by focusing on instabilities of electric power networks that can cause blackouts. It proposes novel approaches to analyze these instabilities and to design cyber-physical control methods to monitor, detect, and mitigate them. The controls must perform robustly in the presence of variability and uncertainty in electric generation, loads, communications, and equipment status, and during abnormal states caused by natural faults or malicious attacks. The research produces cyber-physical engineering methodologies that specifically help to mitigate power system blackouts and more generally show the way forward in designing robust cyber-physical systems in environments characterized by rich dynamics and uncertainty. Education and outreach efforts involve students at high school, undergraduate, and graduate levels, as well as dissemination of results to the public and the engineering and applied science communities in industry, government and universities.

The objective of this research is to develop methods for the operation and design of cyber physical systems in general, and energy efficient buildings in particular. The approach is to use an integrated framework: create models of complex systems from data; then design the associated sensing-communication-computation-control system; and finally create distributed estimation and control algorithms, along with execution platforms to implement these algorithms. A special emphasis is placed on adaptation. In particular, buildings and their environments change with time, as does the way in which buildings are used. The system must be designed to detect and respond to such changes. The proposed research brings together ideas from control theory, dynamical systems, stochastic processes, and embedded systems to address design and operation of complex cyber physical systems that were previously thought to be intractable. These approaches provide qualitative understanding of system behavior, algorithms for control, and their implementation in a networked execution platform. Insights gained by the application of model reduction and adaptation techniques will lead to significant developments in the underlying theory of modeling and control of complex systems. The research is expected to directly impact US industry through the development of integrated software-hardware solutions for smart buildings. Collaborations with United Technologies Research Center are planned to enhance this impact. The techniques developed are expected to apply to other complex cyber-physical systems with uncertain dynamics, such as the electric power grid. The project will enhance engineering education through the introduction of cross-disciplinary courses.

The objective of this research is to develop methods for the operation and design of cyber physical systems in general, and energy efficient buildings in particular. The approach is to use an integrated framework: create models of complex systems from data; then design the associated sensing-communication-computation-control system; and finally create distributed estimation and control algorithms, along with execution platforms to implement these algorithms. A special emphasis is placed on adaptation. In particular, buildings and their environments change with time, as does the way in which buildings are used. The system must be designed to detect and respond to such changes. The proposed research brings together ideas from control theory, dynamical systems, stochastic processes, and embedded systems to address design and operation of complex cyber physical systems that were previously thought to be intractable. These approaches provide qualitative understanding of system behavior, algorithms for control, and their implementation in a networked execution platform. Insights gained by the application of model reduction and adaptation techniques will lead to significant developments in the underlying theory of modeling and control of complex systems. The research is expected to directly impact US industry through the development of integrated software-hardware solutions for smart buildings. Collaborations with United Technologies Research Center are planned to enhance this impact. The techniques developed are expected to apply to other complex cyber-physical systems with uncertain dynamics, such as the electric power grid. The project will enhance engineering education through the introduction of cross-disciplinary courses.

The objective of this research is to develop methods for the operation and design of cyber physical systems in general, and energy efficient buildings in particular. The approach is to use an integrated framework: create models of complex systems from data; then design the associated sensing-communication-computation-control system; and finally create distributed estimation and control algorithms, along with execution platforms to implement these algorithms. A special emphasis is placed on adaptation. In particular, buildings and their environments change with time, as does the way in which buildings are used. The system must be designed to detect and respond to such changes. The proposed research brings together ideas from control theory, dynamical systems, stochastic processes, and embedded systems to address design and operation of complex cyber physical systems that were previously thought to be intractable. These approaches provide qualitative understanding of system behavior, algorithms for control, and their implementation in a networked execution platform. Insights gained by the application of model reduction and adaptation techniques will lead to significant developments in the underlying theory of modeling and control of complex systems. The research is expected to directly impact US industry through the development of integrated software-hardware solutions for smart buildings. Collaborations with United Technologies Research Center are planned to enhance this impact. The techniques developed are expected to apply to other complex cyber-physical systems with uncertain dynamics, such as the electric power grid. The project will enhance engineering education through the introduction of cross-disciplinary courses.

The objective of this research is to develop methods to monitor and ensure the robustness of a class of cyber-physical systems termed "physical networks," such as electric, water, sewage, and gas networks. The approach is to analyze such networks using the mathematical formalism of graphical models. The project models a physical network as a graph, whose variables have a concrete physical interpretation, such as voltage, satisfying known physical laws, such as Kirchoff laws. The machinery of graphical models is used to develop methods to monitor and ensure the robustness of such networks, using the electric power network as a representative. By studying puzzling network-wide interactions, the project has the potential to clarify the role of complexity in large scale networks. Potential contributions will be made to the fields of distributed inference algorithms and fast numerical methods. Physical networks play a crucial role in modern society, and yet, often exhibit fragile behavior, such as black-outs in electric power networks, resulting in economic loss, as well as causing a security risk. This project seeks to understand the robustness behavior of such networks and to train a broad class of students in their theory and practice. Results from this research are to be incorporated into courses and disseminated via research publications. The Carnegie Mellon Conference on the Electricity Industry allows students to interact with faculty and electricity industry veterans. Interaction with the electricity industry aims to provide it with an understanding of cyber-intelligence, to ensure effective robustness monitoring capabilities in the power grid.

The objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications, similar to the way users connect to the Internet. The approach is to view the overall grid management as an adaptive optimizer to iteratively solve a system-wide optimization problem, where networked sensing, control and verification carry out distributed computation tasks to achieve reliability at all levels, particularly component-level, system-level, and application level. Intellectual merit. Under the common theme of reliability guarantees, distributed monitoring and inference algorithms will be developed to perform fault diagnosis and operate resiliently against all hazards. To attain high reliability, a trustworthy middleware will be used to shield the grid system design from the complexities of the underlying software world while providing services to grid applications through message passing and transactions. Further, selective load/generation control using Automatic Generation Control, based on multi-scale state estimation for energy supply and demand, will be carried out to guarantee that the load and generation in the system remain balanced. Broader impact. The envisioned architecture of the smart grid is an outstanding example of the CPS technology. Built on this critical application study, this collaborative effort will pursue a CPS architecture that enables embedding intelligent computation, communication and control mechanisms into physical systems with active and reconfigurable components. Close collaborations between this team and major EMS and SCADA vendors will pave the path for technology transfer via proof-of-concept demonstrations.