Current
- A GPU- based High Performance Computing Cluster for Multiple Military Modeling Capabilities, Air Force Office of Scientific Research, May 2009 to May 2012, $150,000 (PI, Venayagamoorthy).
- EFRI-COPN: Neuroscience and Neural Networks for Engineering the Future Intelligent Electric Power Grid, National Science Foundation, November 2008 to October 2012, $2,000,183 (PI, Venayagamoorthy).
- GOALI: Neural Networks and Adaptive Critic Designs for Energy Security and Sustainability, National Science Foundation, May 2008 to June 2012, $192,311 (PI, Venayagamoorthy).
- Advanced Digital Power Laboratory, Office of Naval Research, April 2008 to May 2012, $488,997 (PI, Venayagamoorthy).
- ONR YIP – The Intelligent All-Electric Ship Power System, Office of Naval Research, January 2007 to May 2012, $405,000 (PI, Venayagamoorthy).
- 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.
