HVDC Distribution Study of Intelligent Power System

University of Dayton Research Institute

SCOPE
High Voltage Direct Current (HVDC) aviation electrical power systems (EPS) provide many advantages, particularly in the area of weight savings. Despite the advantages, there are technical challenges for these systems as the power and dynamic response demanded by high power and more-electric loads increases. High power HVDC systems require low source impedance which makes larger fault energy available to the system. In addition, flight and mission critical loads demand constant power and fast response by a tightly regulated EPS. These loads on a HVDC distribution can cause dynamically negative resistance resulting in poor power quality and/or loss of system stability.

OBJECTIVES
AFRL’ s objective is to develop an intelligent power system to advance the state of the art in system efficiency and safety. This is a far-reaching and broad area of research that is best served by the participation of multiple research institutions that have developed expertise in specific areas. To that end, this Statement of Objectives outlines work where Michigan Technological University (MTU) has demonstrated outstanding research.
Specific areas of research that AFRL is interested in having MTU participate in this program are outlined below. The results of this research and development effort shall be available to all other parties collaborating on the AFRL Intelligent Power System Program as well as industry concerns involved with United States aviation power systems so that best practices and recommendations can be incorporated in future power system design concepts.

RESEARCH TASKS
1.1 Analysis, Design, and Control of components (ns – ms level)
1.2 Distributed management/optimization of source and loads (ms – s level):
1.3 Mission level load planning(> 1 s level)
1.4 Energy Storage (ES) for pulsed power loads

Investigators: Wayne Weaver, Gordon Parker

Bo Chen


Biography

Dr. Chen is the Dave House Associate Professor of Mechanical Engineering and Electrical Engineering in the Department of Mechanical Engineering – Engineering Mechanics and Department of Electrical and Computer Engineering at Michigan Technological University. She received her Ph.D. degree from the University of California, Davis, in 2005. Dr. Chen conducts interdisciplinary researches in the areas of mechatronics and embedded systems, agent technology, modeling and control of hybrid electric vehicles, cyber-physical systems, and automation. Her research projects are funded by National Science Foundation, Department of Energy, and industrial partners. Dr. Chen has authored or co-authored over 70 peer-reviewed journal and conference papers. She received the Best Paper Award at 2008 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications.

Dr. Chen is currently serving as the Chair of the Technical Committee on Mechatronics and Embedded Systems of IEEE Intelligent Transportation Systems Society and the Chair of the Technical Committee on Mechatronic and Embedded Systems and Applications of ASME Design Engineering Division. She is an Associate Editor of the IEEE Transactions on Intelligent Transportation Systems. Dr. Chen has served as Program Chair, Symposium Chair, and Session Chair for a number of international conferences. She was the General Chair of 2013 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications.
Areas of Expertise
Mechatronics and embedded systems
Agent Technology
Monitoring and control networks
Hybrid electric vehicles
Smart grid

Research Interests
Modeling and control of hybrid electric vehicles
EV-smart grid integration
Distributed monitoring and control
Battery control for HEV and energy storage systems
IC engine management systems
Sensor information fusion

Blackout? Robots to the Rescue

September 25, 2014—

Big disasters almost always result in big power failures. Not only do they take down the TV and fridge, they also wreak havoc with key infrastructure like cell towers.  That can delay search and rescue operations at a time when minutes count.

Now, a team led by Nina Mahmoudian of Michigan Technological University has developed a tabletop model of a robot team that can bring power to places that need it the most.

“If we can regain power in communication towers, then we can find the people we need to rescue,” says Mahmoudian, an assistant professor of mechanical engineering–engineering mechanics. “And the human rescuers can communicate with each other.”

Unfortunately, cell towers are often located in hard-to-reach places, she says. “If we could deploy robots there, that would be the first step toward recovery.”

The team has programmed robots to restore power in small electrical networks, linking up power cords and batteries to light a little lamp or set a flag to waving with a small electrical motor. The robots operate independently, choosing the shortest path and avoiding obstacles, just as you would want them to if they were hooking up an emergency power source to a cell tower. To view the robots in action, see the video posted on Mahmoudian’s website.

“Our robots can carry batteries, or possibly a photovoltaic system or a generator,” Mahmoudian said. The team is also working with Wayne Weaver, the Dave House Associate Professor of Electrical Engineering, to incorporate a power converter, since different systems and countries have different electrical requirements (as anyone who has ever blown out a hair dryer in Spain can attest).

In addition to disaster recovery, their autonomous power distribution system could have military uses, particularly for special forces on covert missions. “We could set up power systems before the soldiers arrive on site, so they wouldn’t have to carry all this heavy stuff,” said Mahmoudian.

The team’s next project is in the works: a full-size, working model of their robot network. Their first robot is a tank-like vehicle donated by Michigan Tech’s Keweenaw Research Center. “This will let us develop path-planning algorithms that will work in the real world,” said Mahmoudian.

The robots could also recharge one another, an application that would be as attractive under the ocean as on land.

During search missions like the one conducted for Malaysia Airlines Flight 370, the underwater vehicles scanning for wreckage must come to the surface for refueling. Mahmoudian envisions a fleet of fuel mules that could dive underwater, charge up the searching robot and return to the mother ship. That way, these expensive search vehicles could spend more time looking for evidence and less time traveling back and forth from the surface.

The team presented a paper describing their work, “Autonomous Power Distribution System,” at the 19th World Congress of the International Federation of Automatic Control, held Aug. 24-29 in Cape Town, South Africa. Coauthors are Mahmoudian, Weaver, mechanical engineering graduate student Barzin Moridian, electrical engineering undergraduate Daryl Bennett and Rush Robinett, the Richard and Elizabeth Henes Professor in Mechanical Engineering.

Funding has been provided by Michigan Tech’s Center for Agile Interconnected Microgrids.

Nina Mahmoudian

Low-Cost Underwater Glider Fleet for Littoral Marine Research

Office of Naval Research

This research is focused on development of innovative practical solutions for control of individual and multiple unmanned underwater vehicles (UUVs) and address challenges such as underwater communication and localization that currently limit UUV use. More specifically, the Nonlinear and Autonomous Systems Laboratory (NAS Lab) team are developing a rigorous framework for analyzing and controlling underwater gliders (UGs) in harsh dynamic environments for the purpose of advancing efficient, collaborative behavior of UUVs.

Underwater gliders are now utilized for much more than long-term, basin-scale oceanographic sampling. In addition to environmental monitoring, UGs are increasingly depended on for littoral surveillance and other military applications. This research will facilitate the transition between academic modeling/simulation problem solving approach to real-world Navy applications. The importance of this research is evident in the Littoral BattleSpace Sensing (LBS) Program contract at the Naval Space and Naval Warfare Systems Command for 150 underwater gliders, designated the LBS-G. These gliders will be operated by the Navy in forward areas to rapidly assess and exploit environmental characteristics to improve the maneuvering of ships and submarines and advance the performance of fleet sensors.

Research results will provide the coordination tools necessary to enable the integration of these efficient and quiet vehicles as part of a heterogeneous network of autonomous vehicles capable of performing complex, tactical missions. The objective is to develop practical, energy-efficient motion control strategies for both individual and multiple UGs while performing in inhospitable, uncertain, and dynamic underwater environments.

The specific goals of this project are twofold. The first goal is to design and fabricate a fleet of low-cost highly maneuverable lightweight underwater gliders. The second goal is to evaluate the capability of the single and multiple developed UGs in littoral zones. The proposed work will develop UGs that would share the buoyancy-driven concept with the first generation of gliders called “legacy gliders.” However, the NAS Lab UGs will be smaller in size, lighter in weight, and lower in price than legacy gliders. This will result in more affordable and novel UG applications. Moreover, the NAS Lab design to development approach allows for technological innovation that overcomes known challenges and responds to unexpected needs that arise during testing. Therefore, the significance of this research is that it will enable implementation of recently developed efficient motion planning algorithms, multi-vehicle coordination algorithms, and extension of these algorithms in realistic conditions where absolute location and orientation of each vehicle is not known and the time-varying flow field is not locally determined.

 

Investigators: Nina Mahmoudian

CPS: Breakthrough: Toward Revolutionary Algorithms for Cyber-Physical Systems Architecture Optimization

National Science Foundation

Design optimization of cyber-physical systems (CPS) includes optimizing the system architecture (topology) in addition to the system variables. Optimizing the system architecture renders the dimension of the design space variable (the number of design variables to be optimized is a variable.) This class of Variable-Size Design Space (VSDS) optimization problems arises in many CPS applications including (1) microgrid design, (2) automated construction, (2) optimal grouping, and (3) space mission design optimization.

Evolutionary Algorithms (EAs) present a paradigm for statistical inference that implements a simplified computational model of the mechanisms embedded in natural evolution, with potential to solve this problem. However, existing EAs cannot optimize among solutions of different architectures because of the inherent strategy for coding the variables in EAs. Existing EAs resembles natural evolution in which a given architecture can evolve by improving the state of its variables but cannot be revolutionized. Inspired by the concept of hidden genes in biology, this project investigates revolutionary optimization algorithms that can optimize among different solution architectures and autonomously develop new architectures that might not be known a priori, yet are more fit solution architectures. Efficacy of the new algorithms for CPS is evaluated in the context of space mission design optimization.

Intellectual Merit:
There is an increasing demand in the scientific community for autonomous design optimization tools that can revolutionize systems designs and capabilities. Most existing optimization algorithms can only search for optimal solutions in a fixed-size design space; and hence they cannot be used for solution architecture optimization. Few existing algorithms can search for optimal solutions in VSDS problems; however these are problem-specific algorithms and cannot be used as a general framework for VSDS optimization. This project investigates the novel concept of hidden genes in coding the variables in evolutionary algorithms so that the resulting algorithms can be used for optimizing VSDS problems. The key innovation in these new algorithms is the new coding strategies. In addition, in this project, the standard operations in EAs will be replaced by new operations that are defined to enable revolutionizing a current population of solution architectures using the new coding strategy. The Pl’s recent research results, in the context of space mission design optimization, demonstrate that the hidden genes optimization algorithms can search for optimal solutions among different solution architectures, revolutionize an initial population of solutions, and construct new solution architectures that are more fit than the initial population solutions.

Investigator: Ossama Abdelkhalik

Mark Vaughn

xdJJr3eLPtUPZrpPVx4z3KTToTZm8-h2bIOB7P4spj0Dr. Vaughn has joined Michigan Tech as a research professor after retiring from Sandia National Laboratories. His research expertise is in the area of mechanical and electromechanical design, stress analysis, dynamics, and innovative applications. He has over 10 patents, and has been the lead on a broad array of projects for the military.

Areas of Expertise

  • Electro-Mechanical Analysis and Design
  • Energy Storage
  • Hydrogen Peroxide Systems
  • Advanced Payloads
  • Robotic Vehicles
  • Biomedical Devices

Steven Y. Goldsmith

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Dr. Steven Y. Goldsmith holds dual appointments in the Mechanical Engineering and Engineering Mechanics Department, and the Electrical and Computer Engineering Department. He is also a Senior Fellow at the Technological Leadership Institute at the University of Minnesota. Dr. Goldsmith spent 32 years with Sandia National Laboratories and retired as Distinguished Member of the Technical Staff in 2011. While at Sandia he developed information and control systems for many different applications, including nuclear weapon testing, particle beam accelerators, seismic array monitoring, arms control and treaty verification, environmental life-cycle analysis, e-commerce and international trade, electric grid coordination, collective robotics, information warfare, and automated cyber defense. His current research efforts are focused on intelligent agent systems and technology, particularly the development of adaptive and multi-agent systems. His current projects involve the application of intelligent agents to “smart” electric grid controls and microgrids and cyber security of distributed control systems.

Areas of Expertise

  • Adaptive Software Systems and Intelligent Agents
  • Environmental Life Cycle Analysis of Microgrids
  • Multi-agent Systems for Microgrid Control
  • Cyber Security of Distributed Control Systems
  • Automated Cyber Warfare
  • Simulation of Large Scale Cyber Conflict

Secure Intelligent Architectures for Coordinating Agile Microgrids Research

Overview

Agile microgrids allow variable, distributed sources and loads to effectively interoperate over a broad range of conditions. Enabling large numbers of autonomously-managed micro-generators and loads must be accomplished through information-intensive architectures that create significant challenges regarding coordination and cyber security.

Active Research Projects

Applications

Research is concerned with developing concepts, techniques, and tools for enabling the design of secure and effective multi-agent systems for agile microgrids:

  • Combine cyber security, secure software, and system design, distributed control, and computational modeling to achieve a resilient and reliable control system design for agile microgrids.
  • Design and implement multi-agent system incorporating advanced distributed controls, intrinsic cyber security and safety.
  • Develop simulation-based microgrid design tools that utilize advanced in secure multi-agent distributed control to assist in microgrid development projects involving variable sources and controlled loads

SecureIntelligentArchitecture