Research Grants Awarded While at Missouri University of Science and Technology, Rolla, USA

1.   Intelligent Facts Controllers for Improved Utilization of Existing Power Generation and   TransmissionAssets, British Council Researcher Exchange Programme Awards, period: Jan-Dec. 2008, $10,350 (PI, Venayagamoorthy).

2.   STTR Phase II: Fault Diagnostics, Prognostics, and Self-Healing Control, US Navy, period: January 2008 - June 2009, $150,000 (PI, Venayagamoorthy).

3.   Computer Go - A Proxy for Key Open Challenges and Opportunities in Computational Intelligence, National Science Foundation, August 2007, $299,121 (Co-PI, Venayagamoorthy).

The objective of this research is to illuminate and narrow the differences between computer and human capabilities by making a 9x9 Go player, and creating the groundwork for a 19 x 19 player. The approach is to: Combine Simultaneous Recurrent Networks with Cellular Neural Networks, and train them via Reinforcement Learning, to analyze influence functions; Create Neurofuzzy rules for known patterns; Compare approaches to move filtering; Develop improved tactical analysis; Bootstrap endgame techniques backwards; Develop optimized hardware; Perform outreach. The intellectual merit of the proposed research is: Go is much harder than Chess and its solution offers more to science. Its subtleties mirror core issues in learning. Creative and original concepts are proposed, as outlined in the approach section above. The PI and Co-PI have been collaborators since 1998, and both have significant research track records in many synergistic projects. The broader impacts of the proposed research are: Improved heuristics for cutting through combinatorial complexity. Creating stronger links between learning architectures and improving their training. Contributions to related applications: Economics, Security Applications, Sensor networks, Embedded systems, Biologically-inspired applications, K-12, international, and underrepresented groups outreach. The dissemination of results will be superior, due to the availability of Go rules and data. The project will benefit society, through an improved ability to automate strategic analysis, through better tools, and through an improved workforce.

4.   GAANN: Advanced Computational Techniques and Real-Time Simulation Studies for the Next Generation Energy Systems, Department of Education, August 2007, $511,524 (PI, Venayagamoorthy).

5.   ONR YIP - The Intelligent All-Electric Ship Power System, Office of Naval Research, January 2007, $405,000 (PI, Venayagamoorthy).

6.   A Digital Power Laboratory for Real-Time Simulation, Analysis and Testing of Advanced Power and Intelligent Control Systems, Office of Naval Research, September 2006, $349,997 (PI, Venayagamoorthy).
 
7.   Modernizing the Undergraduate Power Engineering Curriculum with Real-Time Digital Simulation, National Science Foundation, period: January, 2007 - December, 2009, $151,127 (PI, Venayagamoorthy).

This project is developing a novel, real-time, state-of-the art power system simulation teaching and undergraduate research laboratory that incorporates actual computer-controlled hardware in the simulation loop. These resources are being used to develop and incorporate real-time simulation-based experiments into undergraduate power engineering education. As a part of this project, a new course on real-time power system simulation is being developed and taught, and six existing courses are being transformed to incorporate real-time simulation with hardware-in-the-loop experiments. By incorporating real-time simulations with hardware-in-the-loop the power engineering curriculum is providing students with valuable hands on experience, helping them understand how real power systems and power system elements respond in real-time. Instructional materials and project results are being disseminated by posting the material on a website, by conference and journal papers in both engineering education and power engineering venues, and through the laboratory equipment manufacturer's publications. Evaluation efforts, led by an expert from the University's learning center, are using a mixture of qualitative and quantitative methods to monitor progress, and an external advisory committee with industrial members is overseeing the project. The broader impacts include the dissemination of materials and results, outreach and diversity efforts, and workshops for practicing engineers.

8.   NSF CAREER: Graduate Research Supplement - Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems - period: January - December, 2007, $30,996, ECCS #0348221  (PI, Venayagamoorthy).

9.   Neural Networks for Estimating and Compensating the Nonlinear Characteristics of Nonstationary Complex Systems, starting date: May 2006, $70,650, ECCS # 0601521 (PI, Venayagamoorthy).

 The objective of this research is to find a method of accurately quantifying the distorted currents and voltages created by certain devices in power networks. Distortion causes electromagnetic inference with communication and the fast growing digital world, light flicker, overheating of electric machines and transformers and increased losses in transmission lines. For years utilities and customers have argued about who causes the distortion. Existing measurement techniques can lead to errors of up to 40%. The approach is to use Echo State Networks and Simultaneous Recurrent Neural Networks with super fast learning algorithms (biological inspired algorithms such as particle swarm optimization), and other computational intelligence algorithms, to accurately measure the distortion by monitoring only voltage and current without the need for added transducers. Such fast and powerful neural networks could also be used for closed loop control of the offending nonlinear devices to mitigate the distortion. Broader Benefits. The economic impact of applying brain-like techniques to monitor and control physical processes is significant. Reduced power losses mean savings and more useful power over the same lines. More secure and reliable power systems of high quality are of national interest. Moreover, reduced electromagnetic interference promotes a cleaner more reliable telecommunications and digital environment. Fast intelligent nonlinear controllers will also benefit other real-world high-speed closed loop controlled nonlinear non-stationary processes. There exists a talent shortage in the US in the application of intelligent systems, and the project will train a new generation of professionals, and educators, underrepresented minorities and undergraduates in the multiple fields of the project.

10.   SENSORS: Approximate Dynamic Programming for Dynamic Scheduling and Control in Sensor Networks, National Science Foundation, starting date: September 2005, $ 240,000, ECCS #0625737 (PI, Venayagamoorthy).

This project explores new techniques using concepts of approximate dynamic programming for sensor scheduling and control to provide computationally feasible and optimal/near optimal solutions to the limited and varying bandwidth problem. The concept of virtual sensors for sensor data selection iwill also be used to accelerate management of sensor networks under dynamic communication constraints. The goal is to enhance the operational performance of distributed sensor networks and advance knowledge and understanding on how to carry out dynamic stochastic scheduling and control in sensor networks. A novel local and global dynamic stochastic scheduling and control strategy for a large scale sensor network will be designed and demonstrated with laboratory simulation and real-time laboratory implementations. Methods proposed to carry out efficient data reduction and representation will result in overcoming bandwidth constraints. The algorithms developed using brain-like structures in this proposal will provide optimal scheduling with guaranteed stability. Broader impacts The benefit to the society includes efficiently operated reliable and secure sensor networks of national and global interest for applications including border surveillance, landmine detection, unmanned aerial vehicle, vehicle navigation, forest fire response, critical infrastructures heavily dependent on network of sensors for control such as the electric power grid, etc. The sensor scheduling algorithms that are developed in this proposal are directly applicable to many other well known problems such as the supply chain management in a warehouse where several tens of mobile Personal Digital Assistants (sensors capable of transmitting images, text and voice) interacting with central sophisticated servers provide command and control solutions for smooth delivery of products and maintenance of inventory. The investigators will promote best practices in engineering, science and education by integrating research in teaching. Underrepresented minority students and female students from Electrical and Computer Engineering as well as students from other departments currently enrolled at the universities will be recruited to participate in the research activities of this proposal. Other broader impacts include international collaboration, between the U.S. and Australia on this proposal.

11.   Integrated Control of Wind Farms, Facts Devices and the Power Network Using Neural Networks and Adaptive Critic Designs, National Science Foundation, starting date: August. 2005, $ 130,004, ECCS # 0524183 (PI, Venayagamoorthy).

Intellectual Merit: Building on earlier success with smaller systems, this team will develop general-purpose integrated control systems using brain-like design principles to handle larger and more complex systems than have been ever been controlled in the past using such principles. They will be integrating together the use of adaptive dynamic programming (sometimes called "reinforcement learning" or "adaptive critics"), recurrent neural networks (which provide unique capabilities in approximating nonlinear dynamical systems), learning and adaptation, and particle swarm optimization techniques. They will be developing this integration in the context of managing a large complex real system (initially in computer simulation, and then in the laboratory) dominated by partially observed continuous variables, nonlinearity and random disturbances. Broader benefits: The testbed to be controlled represents large windfarms using the most advanced, affordable and efficient (but hard to manage) systems of wind turbines and electronic power control hardware ("Facts"). The ability to achieve such reliable control and efficiency, at low cost, will be crucial to the goal of supplying 20 percent of the world's electrical energy by wind. It will be crucial to making intermittent power like wind more valuable to the grid - and hence more deserving of larger payments from the grid to wind generators, in a rational market system. The team also has active partnerships with Africa and with Brazil, which can supply some of the advanced low-cost Facts technology needed to achieve success - and perhaps also some additional testbeds. This project may be a crucial step in bring the ideals of an intelligent adaptive power grid into the real world.

12.   NSF CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems, period: June, 2004 – May, 2009, $ 400,000, ECCS #0348221 (PI, Venayagamoorthy).

Recently, intelligent techniques and adaptive critic designs have received increasing attention. The dynamic stochastic optimization (DSO) of complex systems such as the electric power grid and its parts can be formulated as minimization and/or maximization of certain quantities. The electric power grid is faced with deregulation and an increased demand for high-quality and reliable electricity for our digital economy, and coupled with interdependencies with other critical infrastructures, it is becoming more and more stressed. Intelligent systems technology will play an important role in carrying out DSO to improve the network efficiency and eliminate congestion problems without seriously diminishing reliability and security. This project proposes to investigate ways in which the power grid can be dynamically optimized, as a testbed for advanced brain-like stochastic identifiers and controllers. This project will advance knowledge and understanding on how to carry out optimization of a dynamic stochastic system. A novel local and global dynamic stochastic optimization strategy for a large scale complex system will be designed. The operating safety margins that currently exist on the large complex systems such as the electric power grid will be minimized, thus, allowing maximum utilization of existing resources with increased system reliability and security with optimal settings on devices throughout the entire system. The capability of carrying out dynamic stochastic optimization is the dream of today. This proposal is a first step in unfolding this dream to reality using brain-like systems with learning and adaptation based on approximate dynamic programming, advanced neural networks and other intelligent techniques on complex systems. In addition, system survivability and availability will be increased by improving reliability and fault tolerance of digital hardware, where the critical algorithms are implemented, using evolution and intelligent techniques. Fault tolerant designs to the unpredictable means robustness, security and safety. The project will also include a major component of educational outreach and of international collaboration including intellectual exchange via faculty and student exchanges between the U.S. and Nigeria, and US and Brazil.