Real-Time Power and Intelligent Systems (RTPIS) Laboratory

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

1. SBIR: Detection and Mitigation of Electrical Faults in Medium Voltage DC (MVDC) Architectures, US Navy, Sept. 2009 to March 2010, $21,000 (PI, Venayagamoorthy).
2. NSF CAREER: REU Supplement – Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems – June – August 2009, $6,000, ECCS # 0348221 (PI, Venayagamoorthy).
3. 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).
4. STTR Phase II: Fault Diagnostics, Prognostics, and Self-Healing Control, US Navy, period: January 2008 – June 2009, $150,000 (PI, Venayagamoorthy).
5. 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 9×9 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.

6. GAANN: Advanced Computational Techniques and Real-Time Simulation Studies for the Next Generation Energy Systems, Department of Education, August 2007, $511,524 (PI, Venayagamoorthy).
7. 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).
8. 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.

9. 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).
10. 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.

11. 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.

12. 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.

13. NUTC – Freight Optimization and Development in Missouri – Waterways and Ports Module, University Transportation Centre, period: June – December, 2007, $10,600 (Co-PI, Venayagamoorthy).

Freight Optimization Study – Waterways and Ports Module – period: June – December 2007, $21,200 (Co-PI, Venayagamoorthy).

14. NSF CAREER: REU Supplement – Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems – period: June – August 2007, $6,000, ECCS #0348221  (PI, Venayagamoorthy).
15. SENSORS: REU Supplement – Approximate Dynamic Programming for Dynamic Scheduling and Control in Sensor Networks, National Science Foundation, period: June. 2007 – August 2007, $6,000, ECCS #0625737 (PI, Venayagamoorthy).
    
16. UMSAEP: Bio-Inspired Techniques for the Optimal Control of Power Systems, Missouri University of Science and Technology South African Education Program,- December 2007 $5,000 (PI, Venayagamoorthy).
17. IREE – NSF CAREER: – Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems, period: September, 2006 – September, 2007, $25,000, ECCS #0348221 (PI, Venayagamoorthy).
18. STTR-Navy: Fault Diagnostics, Prognostics, and Self-Healing Control, period: September, 2006 – September, 2007, $45,089 (PI, Venayagamoorthy).
19. SENSORS: REU Supplement – Approximate Dynamic Programming for Dynamic Scheduling and Control in Sensor Networks, National Science Foundation, period: June – August 2006, $6,000, ECCS #0625737 (PI, Venayagamoorthy).
20. NSF CAREER: REU Supplement – Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems, period: June – August, 2006, $6,000, ECCS #0348221 (PI, Venayagamoorthy).
21. Planning visit to Mexico: Intelligent Techniques to Operation, Control and Diagnosis of Power Plants and Power Systems Including Facts Devices, National Science Foundation, starting date: November, 2005, $6,501, (PI- Ronald Harley (Georgia Tech) Co-PI, Venayagamoorthy), OISE #0519161. (This is travel grant and since the amount is small, no subcontract is made to UMR but Venayagamoorthy’s travel is covered by Georgia Tech).

This Americas Program award will support a planning visit proposal from Dr. Ronald Harley of the Georgia Institute of Technology and Dr. Ganesh K. Venayagamoorthy of the Missouri University of Science and Technology-Rolla. The researchers intend to meet with Mexican colleagues Dr. Edgar Sanchez and Dr. Arturo Messina at the Centro de Investigaciones y Estudios Avanzados de IPN (CINVESTAV-IPN) in Guadalajara, and to use the facilities of the Electric Research Institute in Cuernavaca, in order to plan future complementary research aimed at increasing the scope and impact of their current work in neurocontrol of power systems. The collaboration should allow the further analysis and validation of intelligent control algorithms developed by the U.S. side, using the Mexican side’s unique laboratory and simulation systems. The work could lead to improved stability of the electric power grid, thus reducing the possibility of unexpected large power black-outs, and its consequences to society. This, together with the early diagnosis of impending faults in large rotating generators and motors, will increase the reliability and productivity of energy production and usage, resulting in lower environmental impacts and improved productivity. Aside from the intellectual exchanges, the collaboration with their Mexican colleagues will lead to broader impacts in the area of student exchanges, coursework, and the involvement of females and minorities in the project.

22. UMSAEP: Computational Intelligence Techniques Applied to Modeling Herbivore Plant Interactions in African Savannahs, period: January – December, 2005, $5,000 (PI, Venayagamoorthy).
23. US-Army STTR: Fielded Agent-based Geo-Analysis Network (FAGAN), period: August, 2004 – February, 2005, $45,000 (Co-PI, Venayagamoorthy).
24. Neural Network Based Wide Area Coordination and Local Control of Elements in a Large Complex System, National Science Foundation ECCS #0400657, period: August, 2004 – July, 2007, $ 230,000 (Co-PI, Venayagamoorthy).

Many, many researchers have called out for new systems to manage the nation’s infrastructure networks which would be adaptive, self-healing, integrated and intelligent. This project is a unique effort to make those words literally true, by scaling up new approaches to computational intelligence so as to provide greater performance, robustness and foresight in the control of electric power grids. The work will build on prior successes of the PIs, as described, for example, in www.eas.asu.edu/~nsfadp, in which intelligent control allowed generators to stay up and running in the face of disturbances three times as large as what forces a generator shutdown, under the previous best control schemes. It will explore new ways of scaling up to larger systems, involving multiple generators and advanced power switching components, to be managed in an integrated, optimal fashion. It will use power grids as a testbed for truly general concepts of intelligent systems, and of adaptive management of complex systems.

25. US-Nigeria Cooperative Research: Computational Intelligence Techniques for Reactive Power / Voltage Control of Large Power Systems, National Science Foundation, period: August, 2003 – July, 2005, $ 30,000, INT # 0322894 (PI, Venayagamoorthy).

Venayamagoorthy 0322894 Around the world the demand for electric power is dramatically outpacing available generation and transmission resources. Furthermore, an increasing number of digital users require a higher quality of electricity. Although a number of power system devices are now used to minimize system losses and to improve voltage profiles of the power grid, the response of these power system devices with the existing control is slower than the rate at which changes in the system occur. In this joint project a U.S. scientist from the Missouri University of Science and Technology-Rolla and two Nigerian scientists from Abubakar Tafawa Balewa University will apply computational intelligent techniques to improve the control response of these power system devices, thus decreasing system losses and improving system voltage profiles at all times. This project’s research collaboration and educational cooperation will be achieved via faculty and student exchanges between the U.S. and Nigeria. Intelligent coordination of power system devices can improve the quality of service, increase efficiency of the power transmission system, and ultimately decrease the overall production cost. The research findings will have broad application and will contribute to enhancing the operation of electric utilities of both developing and developed countries.

26. U.S.-Brazil Collaborative Research: Feasibility Studies to Implement Neurocontrollers in Real Time in Brazil, National Science Foundation, period: August, 2003 – July, 2005, $33,500, INT # 0305429 (PI, Venayagamoorthy).

This U.S.-Brazil award will support the collaborative research of Drs. Ganesh K. Venayagamoorthy, Ronald Harley, and Donald C. Wunsch, Missouri University of Science and Technology-Rolla to work with Dr. Nelson Martins, Brazilian Utilities Research Center (CEPEL), and Dr. Djalma M. Falcao, Federal University of Rio de Janeiro (COPPE), in Rio de Janeiro, Brazil. This international team will work to improve the productivity of the existing and future Brazilian power generation and transmission systems using the intelligent control techniques developed by the PIs in an earlier project. CEPEL, the Brazilian national electricity research and development organization, will provide a small-signal stability program for the U.S. team to analyze the Brazilian power system. The Brazilians have already carried out various investigations into finding methods to optimize their power system, and are among world leaders in this regard. This project has the potential to lead to nonlinear controllers for reducing costs of energy and transmission. The U.S. electric power grid is comparable to the Brazilian grid in terms of size and challenges faced. The results of this work will benefit both the U.S. and Brazil.

27. Swarm Intelligence for Generator Modeling and Control, Missouri University of Science and Technology Research Board Grant, period: June, 2003 – May, 2004, $24,000 (PI, Venayagamoorthy).
28. SGER: Intelligent Adaptive Control of Flexible Alternating Current Transmission System (Facts) Devices in a Distributed Power Network Containing Turbogenerators, National Science Foundation, period: August 2002 to July 2004, $69,796, ECS # 0231632 (Co-PI, Venayagamoorthy).

“Flexible AC Transmission System” (Facts) devices refer to rapidly switching power semiconductor devices, used in power systems to control the power flow and stabilize voltages. The decentralized nature of their actions may cause deleterious interactions between one Facts device and another, as well as between Facts devices and generators in the system. Currently there is a general lack of understanding as to how to systematically coordinate and stabilize system-wide dynamics via local modulation of the faster Facts devices as well as the slower generators. Nonlinear neurocontrollers offer a solution. This project will evaluate the initial application of neurocontrollers based on different adaptive critic designs to two different types of Facts devices (one at a time) on a multi-machine power system. An SGER form of proposal is chosen because this is preliminary work based on novel ideas, and if successful, the results will be used to generate a regular proposal which will further investigate the interactions between conventionally controlled generators and multiple neurocontrolled Facts devices. The significance of this work lies in: o Research Benefits: Locally placed neurocontrolled Facts devices can provide system wide improved voltage support and stability, thus allowing the entire power system to be operated more efficiently with a smaller stability margin. o Benefits to Society: Economically operated reliable and secure power systems are of national interest. When electricity demand exceeds available supply, it would be beneficial to produce more electrical power per installed Megawatt (and Dollar) of equipment, with the addition of relatively cheap intelligent neurocontrollers.

Research Grants Awarded while at the Durban Institute of Technology, South Africa (1998 to 2001)

1. NRF THRIP-ESKOM (South Africa) Grant to set up the Real Time Power System Studies Centre at Durban Institute of Technology (DIT) – 2001/2002, South African Rand 2.5 million (Initial proposal grant holder (PI-equivalent) prior to leaving DIT, Venayagamoorthy) – $227,273.
2. Continually Online Trained ANNs for Turbogenerator Control, National Research Foundation
   (NRF, South Africa) grant, August 1998 – July 2001, South African Rand 222,400 (PI,
   Venayagamoorthy) -$27,800.
3. ANNs in Speech Processing, National Research Foundation (NRF, South Africa) grant, August 1998 – July 2001, South African Rand 168,000 (PI, Venayagamoorthy) – $21,000.
4. Speaker Identification using ANNs, Telkom (South Africa) Centre of Excellence grant – 1999 to 2001, South African Rand 480,000, (Co-PI, Venayagamoorthy) – $60,000.
5. M L Sultan Technikon Research grant & Conference Grants – South African Rand 25,000 (PI,Venayagamoorthy) -$8,333.