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RESEARCH
Research
Areas:
(1). Computational Intelligence Techniques for Modeling and Control of Smart Grid
Original
research in the field of computational intelligence techniques for modeling and control of distributed energy systems has
been carried out. This research intends to create a framework for the
introduction of distributed power generation in the context of smart grid.
We investigate system security, reliability and planning issues pertaining
to embedded distributed generators, from which the local utilities and
interested parties can benefit.
(2) Dynamic optimization and management
of micro grid
This
project aims to develop intelligent computational tools for efficiency
optimization and management of smart micro grids, and conduct hardware in
the loop testing. In this work, we design, develop, and deploy
innovative technologies for the next generation of smart grids to provide
solutions to the related problems to make the grid more adaptable,
flexible, intelligent and robust.
(3). Intelligent Mobility Modeling and Management
In order to support a wide range of urban transportation
options, mobility management becomes a crucial factor when designing
infrastructure for urban transport networks. Due to the anticipated
increase in the number of vehicles in the future, the networks should be
able to support a huge number of users and their individual requirements
while at the same time optimizing the resources. This research in intelligent
mobility management system aims to develop new decision making technologies
in the field of hybrid intelligent systems which aid the vehicle
scheduling, intelligent congestion-aware routing and energy
management. A new architecture that applies hybrid computational
intelligence concepts to implement a cooperative multi-agent system for
real-time traffic signal control of a complex traffic network has also been
developed. The large-scale traffic signal control problem is divided into
various sub-problems and modularized, hierarchical agent architecture have been used. Coordinated control is achieved by
intelligent agents, which adopt the cooperative distributed problem solving
approach.
(4). Evolutionary Computation for Optimization and
Scheduling
Evolutionary
algorithms, hybridized with heuristic approaches are being developed for
solving real world problems with large number of objectives in constrained
environments. Various types of operators and control parameter settings
that are likely to produce the best results are also investigated.
(5). Multi-agent Systems
This
research involves design of multi-agent systems that deliver high levels of
cooperation and reliability in dynamic and rapidly evolving environments.
These systems are being developed and tested on two large test beds: the
first for traffic network systems and the
other for distributed energy systems.
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